# Best Data Science and Machine Learning Platforms

## How Many Data Science and Machine Learning Platforms Products Does G2 Track?

**Total Products under this Category:** 1,157

### Category Stats (Aug 2026)

- **Average Rating:** 4.46/5 (↑0.01 vs Jul 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** FalkorDB (+5.88%) - Among all products in this category, FalkorDB recorded the largest rating increase compared to last month

_Last updated: August 01, 2026_

## How Does G2 Rank Data Science and Machine Learning Platforms Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 14,400+ Authentic Reviews
- 1,157+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for Data Science and Machine Learning Platforms
 ![G2 Grid® for Data Science and Machine Learning Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.png?focus%5B%5D=10470&focus%5B%5D=21469&focus%5B%5D=1327283&focus%5B%5D=10938&focus%5B%5D=67045&focus%5B%5D=1308796&focus%5B%5D=24457&focus%5B%5D=162504)

Highlighted products: Databricks, Gemini Enterprise Agent Platform, SAS Viya, Snowflake, Google Cloud AutoML, IBM watsonx.data, MATLAB, and Hex.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=sas-sas-viya&focus%5B%5D=snowflake&focus%5B%5D=google-cloud-automl&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=matlab&focus%5B%5D=hex-tech-hex)

**Sponsored**

### Alteryx

Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=692&secure%5Bchosen_at%5D=2026-08-28T23%3A07%3A52Z&secure%5Bdisplayable_resource_id%5D=692&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=692&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=989&secure%5Bresource_id%5D=692&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-science-and-machine-learning-platforms%3Fsource%3Dsearch&secure%5Btoken%5D=363dd8c59bf2647c0600eca900dacba339eb698e9e8dda7bbab2fe1c252d41fb&secure%5Burl%5D=https%3A%2F%2Fwww.alteryx.com%2Ftrial%3Futm_source%3Dg2%26utm_medium%3Dreviewsite%26utm_campaign%3DFY25_Global_AllRegions_AlwaysOn_AllPersonas_IndustryAgnostic%26utm_content%3Dg2_freetrial&secure%5Burl_type%5D=free_trial)

### [Databricks](https://www.g2.com/fr/products/databricks/reviews)

Databricks est l'entreprise de données et d'IA. Plus de 20 000 organisations dans le monde — y compris adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, et 70 % du Fortune 500 — s'appuient sur la plateforme Data + AI de Databricks pour construire et développer des applications de données et d'IA, des analyses et des agents. Basée à San Francisco avec plus de 30 bureaux dans le monde, Databricks offre une plateforme unifiée qui inclut Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse et Unity Catalog. Fondée en 2013 par les créateurs originaux d'Apache Spark™, Delta Lake, MLflow et Unity Catalog, Databricks est construite sur une architecture de lakehouse ouverte qui réunit données, analyses et IA. La plateforme est utilisée par des ingénieurs de données, des scientifiques de données, des analystes, des développeurs, des équipes de machine learning, des équipes d'IA et des utilisateurs professionnels pour collaborer tout au long du cycle de vie des données et de l'IA. Les principales capacités de Databricks incluent : - Ingénierie des données : Construire, automatiser et gérer des pipelines de données batch, en streaming et en temps réel fiables. - Analytique et intelligence d'affaires : Exécuter des analyses SQL, créer des tableaux de bord et permettre aux équipes commerciales d'explorer les données. - Gouvernance des données : Découvrir, sécuriser et gérer les actifs de données et d'IA à travers les équipes, les clouds et les charges de travail. - Apprentissage automatique et IA : Développer des modèles, construire des applications d'IA générative et créer des agents d'IA de qualité production. - Applications de données : Construire et déployer des applications basées sur les données en utilisant des données d'entreprise gouvernées. Disponible sur AWS, Azure et Google Cloud, Databricks aide les organisations à travailler à travers les clouds, à réduire les silos de données et à simplifier la collaboration entre les équipes et les outils. Les clients utilisent Databricks pour des cas d'utilisation tels que la personnalisation client, la détection de fraude, la maintenance prédictive, l'analyse en temps réel, la cybersécurité, la recherche en santé, la gestion des risques financiers, l'optimisation de la chaîne d'approvisionnement et la prise de décision alimentée par l'IA. Databricks est utilisé dans divers secteurs, y compris les services financiers, la santé et les sciences de la vie, le commerce de détail, la fabrication, l'énergie et le secteur public. Les organisations utilisent la plateforme pour moderniser l'infrastructure de données, accélérer l'adoption de l'IA et transformer les données d'entreprise en valeur commerciale.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,332

#### How Do G2 Users Rate Databricks?

- **Application:** 8.7/10 (Category avg: 8.5/10)
- **Service géré:** 8.5/10 (Category avg: 8.3/10)
- **Compréhension du langage naturel:** 8.4/10 (Category avg: 8.2/10)
- **Facilité d’administration:** 8.4/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Databricks?

- **Vendeur:** [Databricks Inc.](https://www.g2.com/fr/sellers/databricks-inc)
- **Site Web de l'entreprise:** databricks.com
- **Année de fondation:** 2013
- **Emplacement du siège social:** San Francisco, CA
- **Twitter:** @databricks  
92,269 abonnés Twitter
- **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bddca64732f61b923d96364e8c8eb35711aab4f98797cb00ab071ff24fbdd392&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3477522%2F&secure%5Burl_type%5D=linkedin_company_website)  
15,627 employés sur LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingénieur de données, Analyste de données
- **Top Industries:** Technologie de l'information et services, Services financiers
- **Company Size:** 47% Large, 38% Medium

#### What Do G2 Reviewers Say About Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Les utilisateurs apprécient la **facilité d'utilisation et les fonctionnalités étendues** de Databricks, simplifiant les tâches d'entreposage de données et d'apprentissage automatique.
- Les utilisateurs apprécient la **facilité d'utilisation** de Databricks, améliorant leur expérience grâce à son interface intuitive et ses fonctionnalités efficaces.
- Les utilisateurs apprécient les **intégrations transparentes avec les services AWS** qui améliorent l'efficacité et soutiennent divers besoins commerciaux.
- Les utilisateurs apprécient la **collaboration fluide** offerte par Databricks, améliorant le travail d'équipe sur les projets de données et le partage d'informations.
- Les utilisateurs apprécient la **large gamme de fonctionnalités analytiques intégrées** dans Databricks, améliorant l'efficacité et la collaboration dans les projets de données.

##### Cons

- Les utilisateurs sont confrontés à une **courbe d'apprentissage abrupte** avec Databricks, car sa complexité peut être déroutante pour les nouveaux venus.
- Les utilisateurs notent que le **coût de Databricks peut être assez élevé** , en particulier pour les grands projets de données et les options gratuites limitées.
- Les utilisateurs trouvent la **courbe d'apprentissage abrupte** de Databricks difficile, en particulier pour ceux qui ne sont pas familiers avec les outils de big data.
- Les utilisateurs trouvent la **complexité** de Databricks difficile, surtout lors de l'installation initiale et de la navigation dans les fonctionnalités avancées.
- Les utilisateurs rencontrent des **défis complexes de configuration** avec Databricks au début, mais le support aide à résoudre les problèmes rapidement.

#### What Are Recent G2 Reviews of Databricks?

**["Plateforme fiable pour construire des pipelines de données évolutifs"](https://www.g2.com/fr/survey_responses/databricks-review-13198355)**

**Rating:** 5.0/5.0 stars

_— aravind k._

[Read full review](https://www.g2.com/fr/survey_responses/databricks-review-13198355)

**["Databricks simplifie l'ETL et l'analyse avec des notebooks évolutifs"](https://www.g2.com/fr/survey_responses/databricks-review-13181721)**

**Rating:** 5.0/5.0 stars

_— Diana C._

[Read full review](https://www.g2.com/fr/survey_responses/databricks-review-13181721)

#### What Are G2 Users Discussing About Databricks?

- [What does Databricks software do?](https://www.g2.com/fr/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote
- [Qu'est-ce que la plateforme d'analytique unifiée de Databricks ?](https://www.g2.com/fr/discussions/what-is-databricks-unified-analytics-platform) - 3 comments
- [Qu'est-ce que Lakehouse dans Databricks ?](https://www.g2.com/fr/discussions/what-is-lakehouse-in-databricks) - 4 comments, 3 upvotes
- [Quelles sont les fonctionnalités de Databricks ?](https://www.g2.com/fr/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes

### [Gemini Enterprise Agent Platform](https://www.g2.com/pt/products/gemini-enterprise-agent-platform/reviews)

A plataforma abrangente do Google Cloud para desenvolvedores construírem, escalarem, governarem e otimizarem agentes e modelos. É um destino único para equipes técnicas construírem agentes que podem transformar aplicações empresariais e fluxos de trabalho em sistemas agênticos poderosos.

**Average Rating:** 4.3/5.0

**Total Reviews:** 729

#### How Do G2 Users Rate Gemini Enterprise Agent Platform?

- **Aplicativo:** 8.3/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 8.3/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 8.5/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.0/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Gemini Enterprise Agent Platform?

- **Vendedor:** [Google](https://www.g2.com/pt/sellers/google)
- **Ano de Fundação:** 1998
- **Localização da Sede:** Mountain View, CA
- **Twitter:** @google  
31,899,995 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 funcionários no LinkedIn®
- **Propriedade:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Software, Cientista de Dados
- **Top Industries:** Software de Computador, Tecnologia da Informação e Serviços
- **Company Size:** 42% Small, 29% Large

#### What Do G2 Reviewers Say About Gemini Enterprise Agent Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** da Plataforma Gemini Enterprise Agent, destacando sua interface amigável para iniciantes e design intuitivo.
- Os usuários valorizam as **capacidades multimodais** do Gemini, aumentando a produtividade em projetos de desenvolvimento de software e automação.
- Os usuários apreciam as **capacidades multimodais** do Gemini, aumentando a produtividade ao entender texto, imagens, código e documentos juntos.
- Os usuários valorizam as **capacidades multimodais** do Gemini, aumentando a produtividade em projetos de desenvolvimento de software e automação.
- Os usuários apreciam as **integrações fáceis** no Gemini Enterprise Agent, que simplificam os fluxos de trabalho e aumentam a produtividade.

##### Cons

- Os usuários acham a plataforma **cara** , especialmente ao considerar o uso de recursos e a documentação desafiadora.
- Os usuários acham a **curva de aprendizado íngreme** com a Plataforma Gemini Enterprise Agent, devido aos seus numerosos componentes e configurações complexos.
- Os usuários acham a **estrutura de preços complexa** da Plataforma Gemini Enterprise Agent confusa e difícil de navegar.
- Os usuários acham a **estrutura de preços complexa** do Gemini Enterprise Agent desafiadora e sugerem simplificá-la para maior clareza.
- Os usuários acham a **curva de aprendizado difícil** da Plataforma Gemini Enterprise Agent esmagadora, especialmente com recursos avançados e integrações.

#### What Are Recent G2 Reviews of Gemini Enterprise Agent Platform?

**["Criação Fácil de Agente de IA"](https://www.g2.com/pt/survey_responses/gemini-enterprise-agent-platform-review-13193916)**

**Rating:** 4.5/5.0 stars

_— Belhaje A._

[Read full review](https://www.g2.com/pt/survey_responses/gemini-enterprise-agent-platform-review-13193916)

**["Ajudou-nos a automatizar o trabalho rotineiro e a economizar horas todas as semanas"](https://www.g2.com/pt/survey_responses/gemini-enterprise-agent-platform-review-13212825)**

**Rating:** 4.5/5.0 stars

_— Pavan Simhadri D._

[Read full review](https://www.g2.com/pt/survey_responses/gemini-enterprise-agent-platform-review-13212825)

#### What Are G2 Users Discussing About Gemini Enterprise Agent Platform?

- [Para que é usada a Google Cloud AI Platform?](https://www.g2.com/pt/discussions/what-is-google-cloud-ai-platform-used-for) - 5 comments, 5 upvotes
- [What software libraries does cloud ML engine support?](https://www.g2.com/pt/discussions/what-software-libraries-does-cloud-ml-engine-support) - 4 comments, 5 upvotes
- [How do I use Google cloud platform for machine learning?](https://www.g2.com/pt/discussions/how-do-i-use-google-cloud-platform-for-machine-learning)
- [Is Google Cloud AI free?](https://www.g2.com/pt/discussions/is-google-cloud-ai-free)
- [What is Google AI platform?](https://www.g2.com/pt/discussions/what-is-google-ai-platform) - 3 comments, 3 upvotes

### [SAS Viya](https://www.g2.com/pt/products/sas-sas-viya/reviews)

O SAS Viya é uma plataforma de dados e IA nativa da nuvem que permite às equipes construir, implantar e escalar IA explicável que impulsiona decisões confiáveis e seguras. Ele une todo o ciclo de vida de dados e IA e capacita as equipes a inovar rapidamente, equilibrando velocidade, automação e governança por design. O Viya unifica gestão de dados, análises avançadas e tomada de decisão em uma única plataforma, para que as organizações possam passar da experimentação para a produção com confiança, entregando impacto comercial mensurável que é seguro, explicável e escalável em qualquer ambiente. As principais capacidades necessárias para entregar decisões confiáveis incluem: • Clareza de ponta a ponta em todo o ciclo de vida de dados e IA, com linhagem embutida, auditabilidade e monitoramento contínuo para apoiar decisões defensáveis. • Governança por design, permitindo supervisão consistente em dados, modelos e decisões para reduzir riscos e acelerar a adoção. • IA explicável em escala, para que insights e resultados possam ser compreendidos, validados e confiáveis tanto por empresas quanto por reguladores. • Análises operacionalizadas, garantindo que o valor continue além da implantação através de monitoramento, re-treinamento e gestão do ciclo de vida. • Implantação flexível e nativa da nuvem, permitindo que as organizações comecem em qualquer lugar e escalem em todos os lugares enquanto mantêm o controle.

**Average Rating:** 4.3/5.0

**Total Reviews:** 774

#### How Do G2 Users Rate SAS Viya?

- **Aplicativo:** 7.8/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 7.9/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 7.8/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 7.6/10 (Category avg: 8.6/10)

#### Who Is the Company Behind SAS Viya?

- **Vendedor:** [SAS Institute Inc.](https://www.g2.com/pt/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Website da Empresa:** www.sas.com
- **Ano de Fundação:** 1976
- **Localização da Sede:** Cary, NC
- **Twitter:** @SASsoftware  
60,863 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=64db42c044af5bbad79bd9677a620a6c31a8ff1abf4e7b2a6f1d6ed9561d105d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1491%2F&secure%5Burl_type%5D=linkedin_company_website)  
18,638 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Estudante, Bioestatístico
- **Top Industries:** Farmacêuticos, Bancário
- **Company Size:** 33% Small, 33% Large

#### What Do G2 Reviewers Say About SAS Viya?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários valorizam a **facilidade de uso** no SAS Viya, aprimorando a visualização de dados e a tomada de decisões para as empresas.
- Os usuários apreciam as **capacidades analíticas avançadas** do SAS Viya, tornando a análise de dados e a tomada de decisões mais eficientes.
- Os usuários valorizam as **capacidades analíticas sofisticadas** do SAS Viya, aprimorando a tomada de decisões e insights a partir de diversas fontes de dados.
- Os usuários valorizam as **ferramentas de ciclo de vida de dados de ponta a ponta** no SAS Viya, aprimorando as capacidades de insights e tomada de decisões estratégicas.
- Os usuários elogiam o SAS Viya por sua **interface amigável** , tornando análises complexas acessíveis a indivíduos de todos os níveis de habilidade.

##### Cons

- Os usuários acham o SAS Viya **difícil para usuários não técnicos** navegarem, impactando a facilidade de acesso a relatórios e painéis.
- Os usuários acham a **curva de aprendizado desafiadora** , especialmente para indivíduos não técnicos que navegam por relatórios e painéis.
- Os usuários acham a **complexidade da visualização** do SAS Viya desafiadora, especialmente para aqueles sem expertise técnica.
- Os usuários acham a **curva de aprendizado difícil** do SAS Viya desafiadora, especialmente para usuários não técnicos que tentam acessar os recursos.
- Os usuários consideram o **preço elevado** do SAS Viya uma barreira potencial, complicando seu processo de tomada de decisão.

#### What Are Recent G2 Reviews of SAS Viya?

**["SAS Viya: Poderosa IA e Análise de Dados com Integrações Sem Costura"](https://www.g2.com/pt/survey_responses/sas-viya-review-11855145)**

**Rating:** 5.0/5.0 stars

_— Usuário Verificado em Hospital e Cuidados de Saúde_

[Read full review](https://www.g2.com/pt/survey_responses/sas-viya-review-11855145)

**["Análise de Dados Eficaz com SAS Viya"](https://www.g2.com/pt/survey_responses/sas-viya-review-11872818)**

**Rating:** 4.5/5.0 stars

_— Fungai J._

[Read full review](https://www.g2.com/pt/survey_responses/sas-viya-review-11872818)

#### What Are G2 Users Discussing About SAS Viya?

- [Para que é usado o SAS Visual Data Mining and Machine Learning?](https://www.g2.com/pt/discussions/what-is-sas-visual-data-mining-and-machine-learning-used-for) - 2 comments

### [Google Cloud AutoML](https://www.g2.com/products/google-cloud-automl/reviews)

Google Cloud AutoML is a suite of machine learning products designed to enable developers with limited expertise to train high-quality custom models tailored to their specific business needs. By leveraging Google's advanced transfer learning and neural architecture search technologies, AutoML simplifies the process of building, deploying, and scaling machine learning models, making AI more accessible to a broader audience. Key Features and Functionality: - Automated Model Training: AutoML automates the selection of model architecture and hyperparameter tuning, reducing the need for manual intervention and specialized knowledge. - User-Friendly Interface: The platform offers an intuitive graphical interface that allows users to upload data, train models, and manage deployments with ease. - Versatile Model Types: AutoML supports various data types and tasks through specialized services: - AutoML Vision: For image classification and object detection. - AutoML Natural Language: For text classification, sentiment analysis, and entity recognition. - AutoML Translation: For creating custom translation models between language pairs. - AutoML Video Intelligence: For video classification and object tracking. - AutoML Tables: For structured data tasks like regression and classification. - Seamless Integration: AutoML integrates with other Google Cloud services, facilitating efficient data management, model deployment, and scalability. Primary Value and Problem Solving: Google Cloud AutoML democratizes machine learning by enabling users without deep technical expertise to develop and deploy custom models. This accessibility allows businesses to harness the power of AI to solve complex problems, such as improving customer experiences through personalized recommendations, automating content moderation, enhancing language translation services, and gaining insights from large datasets. By reducing the barriers to entry, AutoML empowers organizations to innovate and stay competitive in their respective industries.

**Average Rating:** 4.3/5.0

**Total Reviews:** 41

#### How Do G2 Users Rate Google Cloud AutoML?

- **Natural Language Understanding:** 9.4/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Google Cloud AutoML?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 43% Small, 36% Medium

#### What Do G2 Reviewers Say About Google Cloud AutoML?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **seamless AI integration** of Google Cloud AutoML, enhancing productivity without needing deep ML knowledge.
- Users appreciate the **ease of use** of Google Cloud AutoML, enabling quick training of models without deep expertise.
- Users appreciate the **easy integrations** of Google Cloud AutoML, enhancing their machine learning experience effortlessly.
- Users appreciate the **seamless integration** of Google Cloud AutoML, enhancing usability and collaboration with other Google services.
- Users appreciate the **intuitive interface** of Google Cloud AutoML, making machine learning accessible without deep expertise.

##### Cons

- Users find the **cost prohibitive** for smaller projects or students, making it less accessible for them.
- The **pricing can be expensive** for small projects or students, limiting accessibility and usage for some users.

#### What Are Recent G2 Reviews of Google Cloud AutoML?

**["Google Cloud AutoML Helps Us Train ML Models Faster and More Efficiently"](https://www.g2.com/survey_responses/google-cloud-automl-review-13334379)**

**Rating:** 4.0/5.0 stars

_— Kennedy J._

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13334379)

**["Google Cloud AutoML Simplifies Model Building and Deployment"](https://www.g2.com/survey_responses/google-cloud-automl-review-13278558)**

**Rating:** 4.5/5.0 stars

_— LOKESH G._

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13278558)

#### What Are G2 Users Discussing About Google Cloud AutoML?

- [What is Google Cloud AutoML used for?](https://www.g2.com/discussions/what-is-google-cloud-automl-used-for)

## FAQs About Data Science and Machine Learning Platforms

Generated using AI

Last updated: April 27, 2026

### Leading machine learning services for enterprise

Based on G2 reviews, enterprise teams often favor platforms that unify data preparation, model training, deployment, governance, and monitoring in one environment.

- [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — unified ML lifecycle and deployment.
- [Databricks](https://www.g2.com/products/databricks/reviews) — lakehouse workflows with collaborative notebooks.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — large-scale analytics with governance.
- [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) — governed AI development for enterprises.

### Top-rated software for data analysis in SaaS industry

Based on G2 reviews, buyers in software environments often prioritize platforms that shorten analysis cycles, support collaboration, and reduce tool switching.

- [Hex](https://www.g2.com/products/hex-tech-hex/reviews) — SQL, Python, and dashboarding together.
- [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — end-to-end ML workflows in one place.
- [Databricks](https://www.g2.com/products/databricks/reviews) — scalable analytics and ML collaboration.
- [Deepnote](https://www.g2.com/products/deepnote/reviews) — collaborative notebooks for team analysis.

### Which platform offers the best machine learning solutions

Based on G2 reviews, the strongest options depend on whether your team values unified workflows, low-code model building, notebook collaboration, or governance.

- [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — managed training, deployment, and monitoring.
- [Databricks](https://www.g2.com/products/databricks/reviews) — engineering, analytics, and ML together.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — advanced analytics with strong controls.
- [Anaconda Platform](https://www.g2.com/products/anaconda-platform/reviews) — reproducible environments and package management.

### What are data science and machine learning platforms used for

According to verified users, data science and machine learning platforms are used to centralize the work of preparing data, building models, testing ideas, deploying models, and sharing results. Reviews repeatedly mention workflow simplification as a major benefit: teams can reduce tool switching, automate repetitive preparation tasks, and move from experimentation to production with less manual setup. Buyers also use these platforms for dashboards, forecasting, predictive modeling, model monitoring, collaboration across technical and non-technical teams, and connecting data from warehouses, cloud systems, spreadsheets, or operational tools. Common buyer concerns in the reviews include learning curve, documentation quality, cost visibility, and performance on very large workloads.

### How do teams use data science and machine learning platforms for collaboration

According to verified users, collaboration is one of the most practical reasons teams adopt these platforms. Reviews describe analysts, data scientists, and engineers working in shared notebooks, common environments, and governed workspaces so they can move from raw data to analysis, visualizations, and deployed models without passing files back and forth. Teams also mention easier sharing of dashboards, published apps, reusable workflows, and reproducible environments. In several reviews, this reduces friction between technical and non-technical stakeholders because results can be reviewed, discussed, and reused in one place. The strongest collaboration themes in the recent reviews are shared notebooks, consistent environments, versioned workflows, and easier handoffs into production.

### [Snowflake](https://www.g2.com/pt/products/snowflake/reviews)

A Snowflake permite que todas as organizações mobilizem seus dados com o AI Data Cloud da Snowflake. Os clientes usam o AI Data Cloud para unir dados isolados, descobrir e compartilhar dados com segurança, alimentar aplicativos de dados e executar diversas cargas de trabalho de IA/ML e analíticas. Onde quer que os dados ou usuários estejam, a Snowflake oferece uma experiência de dados única que abrange várias nuvens e geografias. Milhares de clientes em muitos setores, incluindo 691 dos 2000 maiores do mundo segundo a Forbes em 2023 (G2K) até 31 de janeiro, usam o AI Data Cloud da Snowflake para impulsionar seus negócios.

**Average Rating:** 4.5/5.0

**Total Reviews:** 716

#### How Do G2 Users Rate Snowflake?

- **Aplicativo:** 9.2/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 9.0/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 8.6/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.7/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Snowflake?

- **Vendedor:** [Snowflake, Inc.](https://www.g2.com/pt/sellers/snowflake-inc)
- **Website da Empresa:** www.snowflake.com
- **Ano de Fundação:** 2012
- **Localização da Sede:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ad18ff73a9b8bb34dd1b98a6ba1c6be57f7364939ad352612ecc483aba05d2b2&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsnowflake-computing%2F&secure%5Burl_type%5D=linkedin_company_website)  
11,308 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Analista de Dados
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 45% Medium, 42% Large

#### What Do G2 Reviewers Say About Snowflake?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Snowflake, que simplifica o compartilhamento de dados e aumenta a produtividade entre as equipes.
- Os usuários valorizam os **recursos confiáveis e a interface amigável** do Snowflake, melhorando a eficiência do gerenciamento de dados e análises.
- Os usuários apreciam a **facilidade de uso e a integração eficiente de dados** no Snowflake para seus projetos de armazenamento.
- Os usuários valorizam a **escalabilidade contínua** do Snowflake, permitindo o manuseio eficiente de grandes conjuntos de dados e mudanças de carga de trabalho sem perda de desempenho.
- Os usuários valorizam as **capacidades de processamento de dados rápidas e eficientes** do Snowflake, melhorando significativamente sua experiência de análise.

##### Cons

- Os usuários destacam os **altos custos** do Snowflake, tornando-o menos acessível para pequenas empresas com orçamentos limitados.
- Os usuários acham **as limitações de recursos** no Snowflake, como a falta de blocos de código e permissões restritas, frustrantes.
- Os usuários acham a **curva de aprendizado íngreme** , exigindo treinamento devido à sua complexidade e interface avassaladora para iniciantes.
- Os usuários muitas vezes enfrentam dificuldades com **altos custos** devido a consultas não otimizadas e medidas inadequadas de controle de custos no Snowflake.
- Os usuários acham a **estrutura de custos desafiadora** , exigindo tempo para otimizar o uso eficiente do Snowflake.

#### What Are Recent G2 Reviews of Snowflake?

**["Snowflake Simplifica o Gerenciamento de Dados em Escala"](https://www.g2.com/pt/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

[Read full review](https://www.g2.com/pt/survey_responses/snowflake-review-12898129)

**["Escalonamento Elástico e Análises Rápidas com Snowflake"](https://www.g2.com/pt/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/pt/survey_responses/snowflake-review-13129003)

#### What Are G2 Users Discussing About Snowflake?

- [What is Snowflake used for?](https://www.g2.com/pt/discussions/what-is-snowflake-used-for) - 2 comments, 1 upvote

### [IBM watsonx.data](https://www.g2.com/pt/products/ibm-watsonx-data/reviews)

IBM® watsonx.data® ajuda você a acessar, integrar e entender todos os seus dados — estruturados e não estruturados — em qualquer ambiente. Ele otimiza cargas de trabalho para preço e desempenho enquanto aplica governança consistente em todas as fontes, formatos e equipes. Assista à demonstração para aprender como o watsonx.data capacita você a construir aplicativos de IA generativa e agentes de IA poderosos. Teste gratuito disponível: https://ibm.biz/Watsonx-data\_Trial

**Average Rating:** 4.4/5.0

**Total Reviews:** 167

#### G2 Deal: Save 30% on your first monthly or annual subscription. Offer ends 15 April 2026.

Get 30% off your new monthly or annual watsonx.data Enterprise subscription. Optimize data workloads at a fraction of the cost. Offer ends 15 April 2026.

**Price:** ~~61.81~~ → 88.30

[View this exclusive G2 deal](https://www.g2.com/pt/deals/offers/245ee783-2209-473a-beb1-3df6bd40f9ef?source=categories)

#### How Do G2 Users Rate IBM watsonx.data?

- **Aplicativo:** 5.8/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 7.2/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 8.0/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.0/10 (Category avg: 8.6/10)

#### Who Is the Company Behind IBM watsonx.data?

- **Vendedor:** [IBM](https://www.g2.com/pt/sellers/ibm)
- **Website da Empresa:** www.ibm.com
- **Ano de Fundação:** 1911
- **Localização da Sede:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Software, CEO
- **Top Industries:** Software de Computador, Tecnologia da Informação e Serviços
- **Company Size:** 34% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.data?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do IBM watsonx.data, achando-o confiável e eficiente para gerenciamento de dados.
- Os usuários valorizam a **integração de dados perfeita** e a interface amigável do IBM watsonx.data para análises eficientes.
- Os usuários apreciam a **gestão de dados organizada e eficiente** do IBM watsonx.data, simplificando a análise e melhorando a colaboração da equipe.
- Os usuários valorizam a **integração perfeita de fontes de dados** do IBM watsonx.data, aumentando a eficiência e flexibilidade em seus fluxos de trabalho.
- Os usuários apreciam as **capacidades flexíveis de análise** do IBM watsonx.data, permitindo insights mais rápidos a partir de diversas fontes de dados.

##### Cons

- Os usuários acham a **curva de aprendizado íngreme** do IBM watsonx.data desafiadora, dificultando a adoção fácil para os novatos.
- Os usuários consideram a **complexidade** de configurar o IBM watsonx.data uma barreira, especialmente para iniciantes e pequenas equipes.
- Os usuários acham o **preço elevado** para o IBM watsonx.data, especialmente para pequenas empresas com recursos limitados.
- Os usuários acham o **processo de configuração difícil** demorado, com uma curva de aprendizado acentuada e uma revisão extensa da documentação necessária.
- Os usuários acham **difícil otimizar o desempenho** com o IBM watsonx.data, especialmente para iniciantes e equipes com recursos de TI limitados.

#### What Are Recent G2 Reviews of IBM watsonx.data?

**["Plataforma de Dados Flexível e Escalável para Análise e IA"](https://www.g2.com/pt/survey_responses/ibm-watsonx-data-review-13229034)**

**Rating:** 5.0/5.0 stars

_— Nishant V._

[Read full review](https://www.g2.com/pt/survey_responses/ibm-watsonx-data-review-13229034)

**["Interface limpa e suave com excelente integração e visuais de infraestrutura"](https://www.g2.com/pt/survey_responses/ibm-watsonx-data-review-13204444)**

**Rating:** 4.0/5.0 stars

_— Aliasgar B._

[Read full review](https://www.g2.com/pt/survey_responses/ibm-watsonx-data-review-13204444)

### [Hex](https://www.g2.com/pt/products/hex-tech-hex/reviews)

Hex é a plataforma de análise de IA favorita do mundo. Com o Hex, qualquer pessoa pode explorar dados usando linguagem natural, com ou sem código, tudo em um contexto confiável, em uma plataforma única e alimentada por IA. [Comece agora](https://app.hex.tech/signup?source=g2)[Solicite uma demonstração](https://hex.tech/request-a-demo/?source=g2)

**Average Rating:** 4.5/5.0

**Total Reviews:** 403

#### How Do G2 Users Rate Hex?

- **Aplicativo:** 6.9/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 6.8/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 5.1/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 9.0/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Hex?

- **Vendedor:** [Hex Tech](https://www.g2.com/pt/sellers/hex-tech)
- **Website da Empresa:** hex.tech
- **Ano de Fundação:** 2019
- **Localização da Sede:** San Francisco, US
- **Twitter:** @\_hex\_tech  
6,982 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=18531fc4e6d51a8771f4a2f229024fea767874199650a484a62b89cd4abe5f24&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fhex-technologies%2F&secure%5Burl_type%5D=linkedin_company_website)  
280 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Analista de Dados, Cientista de Dados
- **Top Industries:** Software de Computador, Tecnologia da Informação e Serviços
- **Company Size:** 53% Medium, 22% Small

#### What Do G2 Reviewers Say About Hex?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários consideram o Hex **fácil de usar** , destacando suas integrações perfeitas e rápida implementação como principais vantagens.
- Os usuários adoram a **integração perfeita do SQL com Python** , aprimorando suas capacidades analíticas no Hex sem esforço.
- Os usuários apreciam as capacidades de **gestão de dados sem interrupções** do Hex, permitindo integração e colaboração sem esforço.
- Os usuários valorizam a **integração sem esforço de SQL e Python** no Hex, aprimorando as capacidades de análise e visualização de dados.
- Os usuários apreciam as capacidades de **análise de dados e relatórios sem interrupções** do Hex, melhorando a colaboração e a interatividade.

##### Cons

- Os usuários criticam os **recursos limitados** do Hex, observando capacidades insuficientes em comparação com ferramentas de BI padrão como o Tableau.
- Os usuários acham os **recursos ausentes** no Hex frustrantes, desejando melhores capacidades de visualização de dados e gerenciamento de resultados.
- Os usuários acham que o Hex **carece de recursos** , especialmente em painéis e funcionalidades avançadas que impactam a usabilidade e a eficiência.
- Os usuários experimentam **desempenho lento** com o Hex, especialmente em máquinas virtuais e devido à capacidade computacional limitada.
- Os usuários frequentemente enfrentam **problemas de gerenciamento de dados** com falhas de kernel e integração de GPU, complicando sua experiência com o Hex.

#### What Are Recent G2 Reviews of Hex?

**["Incrível IA e Autocompletar SQL que Acelera Meu Trabalho"](https://www.g2.com/pt/survey_responses/hex-review-12687305)**

**Rating:** 4.0/5.0 stars

_— Paco R._

[Read full review](https://www.g2.com/pt/survey_responses/hex-review-12687305)

**["Espaço de Trabalho Colaborativo Tudo-em-Um para SQL, Python e Painéis Interativos"](https://www.g2.com/pt/survey_responses/hex-review-13125920)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/pt/survey_responses/hex-review-13125920)

#### What Are G2 Users Discussing About Hex?

- [Para que é usada a Hex Technologies?](https://www.g2.com/pt/discussions/what-is-hex-technologies-used-for)

### [MATLAB](https://www.g2.com/pt/products/matlab/reviews)

O MATLAB é um ambiente de programação de alto nível e computação numérica amplamente utilizado por engenheiros e cientistas para análise de dados, desenvolvimento de algoritmos e modelagem de sistemas. Ele oferece um ambiente de trabalho otimizado para processos de análise e design iterativos, juntamente com uma linguagem de programação que expressa diretamente a matemática de matrizes e arrays. O recurso Live Editor permite que os usuários criem scripts que integram código, saída e texto formatado dentro de um notebook executável. Principais Recursos e Funcionalidades: - Análise de Dados: Ferramentas para explorar, modelar e analisar dados. - Gráficos: Funções para visualizar e explorar dados através de vários gráficos e diagramas. - Programação: Capacidades para criar scripts, funções e classes para fluxos de trabalho personalizados. - Construção de Aplicativos: Facilidades para desenvolver aplicativos de desktop e web. - Interfaces de Linguagem Externa: Integração com linguagens como Python, C/C++, Fortran e Java. - Conectividade de Hardware: Suporte para conectar o MATLAB a várias plataformas de hardware. - Computação Paralela: Capacidade de realizar cálculos em grande escala e paralelizar simulações usando desktops multicore, GPUs, clusters e recursos em nuvem. - Implantação: Opções para compartilhar programas MATLAB e implantá-los em aplicativos empresariais, dispositivos embarcados e ambientes em nuvem. Valor Principal e Soluções para Usuários: O MATLAB simplifica cálculos matemáticos complexos e tarefas de análise de dados, permitindo que os usuários desenvolvam algoritmos e modelos de forma eficiente. Suas ferramentas abrangentes e aplicativos interativos facilitam a prototipagem rápida e o design iterativo, reduzindo o tempo de desenvolvimento. A escalabilidade da plataforma permite uma transição suave da pesquisa para a produção, suportando a implantação em vários sistemas sem modificações extensas de código. Ao integrar-se com múltiplas linguagens de programação e plataformas de hardware, o MATLAB oferece um ambiente versátil que atende às diversas necessidades de engenheiros e cientistas em diferentes indústrias.

**Average Rating:** 4.5/5.0

**Total Reviews:** 752

#### How Do G2 Users Rate MATLAB?

- **Aplicativo:** 8.6/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 8.3/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 8.5/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.4/10 (Category avg: 8.6/10)

#### Who Is the Company Behind MATLAB?

- **Vendedor:** [MathWorks](https://www.g2.com/pt/sellers/mathworks)
- **Ano de Fundação:** 1984
- **Localização da Sede:** Natick, MA
- **Twitter:** @MATLAB  
105,142 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=6dbb518e9679f4f3158244b1c49a1caf60257ab06a669303d999c534715b2674&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1194036%2F&secure%5Burl_type%5D=linkedin_company_website)  
7,985 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Estudante, Assistente de Pesquisa de Pós-Graduação
- **Top Industries:** Educação Superior, Pesquisa
- **Company Size:** 42% Large, 31% Small

#### What Do G2 Reviewers Say About MATLAB?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **interface amigável** do MATLAB, tornando a visualização e manipulação de dados fácil e eficiente.
- Os usuários apreciam as **ferramentas de visualização poderosas** do MATLAB, aprimorando as capacidades de plotagem de dados em tempo real e processamento de imagens.
- Os usuários apreciam os **recursos poderosos e fáceis de usar de visualização de dados** do MATLAB para plotagem em tempo real.
- Os usuários apreciam a **variedade de ferramentas** no MATLAB, aprimorando as capacidades em análise numérica, processamento de imagens e simulações.
- Os usuários apreciam a **facilidade das simulações** com o MATLAB, especialmente com sua integração perfeita do Simulink para diversas aplicações.

##### Cons

- Os usuários acham o MATLAB **caro** , tornando difícil para indivíduos e pequenas empresas arcar com o custo.
- Os usuários frequentemente experimentam **desempenho lento** com o MATLAB, especialmente em máquinas menos potentes ou com grandes conjuntos de dados.
- Os usuários acham os **altos requisitos de sistema** do MATLAB frustrantes, muitas vezes levando a um desempenho mais lento em máquinas menos potentes.
- Os usuários consideram o **licenciamento caro** do MATLAB uma barreira significativa, especialmente para indivíduos e pequenas empresas.
- Os usuários frequentemente encontram **desempenho lento** com o MATLAB, especialmente durante grandes simulações e com múltiplos scripts abertos.

#### What Are Recent G2 Reviews of MATLAB?

**["MATLAB Amigável com Interface de Usuário de Primeira Linha, Ferramentas e Desempenho de Simulação"](https://www.g2.com/pt/survey_responses/matlab-review-12675291)**

**Rating:** 5.0/5.0 stars

_— Usuário Verificado em Educação Superior_

[Read full review](https://www.g2.com/pt/survey_responses/matlab-review-12675291)

**["Um Potente Centro para Simulações e Modelagem de Engenharia Avançada"](https://www.g2.com/pt/survey_responses/matlab-review-12689149)**

**Rating:** 4.0/5.0 stars

_— Wafa M._

[Read full review](https://www.g2.com/pt/survey_responses/matlab-review-12689149)

#### What Are G2 Users Discussing About MATLAB?

- [Para que é usado o MATLAB?](https://www.g2.com/pt/discussions/what-is-matlab-used-for) - 1 comment
- [Posso usar o Matlab de graça?](https://www.g2.com/pt/discussions/can-i-use-matlab-for-free) - 3 comments
- [Em que linguagem o Matlab é escrito?](https://www.g2.com/pt/discussions/what-is-matlab-written-in) - 1 comment
- [O Matlab é uma linguagem de programação ou um software?](https://www.g2.com/pt/discussions/is-matlab-a-programming-language-or-software) - 1 comment
- [Para que é usado o software Matlab?](https://www.g2.com/pt/discussions/what-is-matlab-software-used-for) - 1 comment

### [Dataiku](https://www.g2.com/pt/products/dataiku/reviews)

Dataiku é a Plataforma para o Sucesso em IA: a camada de orquestração de IA onde as empresas constroem, implantam e governam análises, modelos e agentes em escala. Ela se posiciona sobre as plataformas de dados, nuvens e serviços de IA que você já utiliza, funcionando em todos eles sem prendê-lo a nenhum. Dataiku amplia quem pode construir IA de produção, colocando as ferramentas certas nas mãos de cientistas de dados e especialistas de domínio, desde analistas de fraude até planejadores de demanda. Ela orquestra aprendizado de máquina, regras, LLMs e agentes como um sistema governado, construído com base em mais de uma década de execução de IA em produção. A governança é parte da construção, em vez de algo adicionado posteriormente, permitindo que as equipes entreguem mais rápido enquanto mantêm o desempenho, custo e risco sob controle. O resultado: IA que passa da experimentação para uma execução confiável e mensurável agora, não em 18 meses.

**Average Rating:** 4.4/5.0

**Total Reviews:** 215

#### How Do G2 Users Rate Dataiku?

- **Aplicativo:** 8.3/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 8.2/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 7.8/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.0/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Dataiku?

- **Vendedor:** [Dataiku](https://www.g2.com/pt/sellers/dataiku)
- **Website da Empresa:** Dataiku.com
- **Ano de Fundação:** 2013
- **Localização da Sede:** New York, NY
- **Twitter:** @dataiku  
22,917 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e59ec8fccc02ecc4f883419e54da56d3f6fc8b1e556153f0cc01cd05e3b77faa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataiku%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,619 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Cientista de Dados, Analista de Dados
- **Top Industries:** Serviços Financeiros, Farmacêuticos
- **Company Size:** 60% Large, 23% Medium

#### What Do G2 Reviewers Say About Dataiku?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam como o **Dataiku simplifica o desenvolvimento de ML** , permitindo o treinamento, avaliação e compreensão de dados de forma rápida e fácil.
- Os usuários acham o Dataiku **fácil de usar** , simplificando o desenvolvimento de ML e ajudando a detectar oportunidades e riscos sem esforço.
- Os usuários valorizam a **facilidade de uso** no Dataiku, permitindo a colaboração e simplificando processos de dados complexos para todos os níveis de habilidade.
- Os usuários apreciam as **integrações fáceis** do Dataiku, facilitando a colaboração entre diversas ferramentas de análise e conjuntos de habilidades.
- Os usuários elogiam a **melhoria na produtividade** trazida pelas receitas visuais do Dataiku e pelas ferramentas robustas para projetos de análise.

##### Cons

- Os usuários acham a **curva de aprendizado íngreme** , tornando desafiador para iniciantes utilizarem plenamente os recursos avançados do Dataiku.
- Os usuários acham a **curva de aprendizado acentuada** desafiadora, especialmente para iniciantes que navegam pelas funcionalidades avançadas do Dataiku.
- Os usuários acham a **curva de aprendizado difícil** desafiadora para iniciantes, impactando sua capacidade de maximizar o potencial da plataforma.
- Os usuários enfrentam **desempenho lento** com o Dataiku ao gerenciar grandes conjuntos de dados, impactando a eficiência e a produtividade.
- Os usuários acham o **preço alto** para pequenas empresas e estudantes, impactando a acessibilidade para projetos básicos.

#### What Are Recent G2 Reviews of Dataiku?

**["Construa fluxos de trabalho mais rápidos com dados conectados de muitos provedores ou fontes de dados distintas"](https://www.g2.com/pt/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

[Read full review](https://www.g2.com/pt/survey_responses/dataiku-review-13120436)

**["Plataforma Unificada de Baixo Código que Aumenta a Produtividade de Dados e IA de Ponta a Ponta"](https://www.g2.com/pt/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/pt/survey_responses/dataiku-review-13125252)

#### What Are G2 Users Discussing About Dataiku?

- [Is Dataiku an ETL tool?](https://www.g2.com/pt/discussions/is-dataiku-an-etl-tool)
- [Is Dataiku web based?](https://www.g2.com/pt/discussions/is-dataiku-web-based)
- [What is DSS Dataiku?](https://www.g2.com/pt/discussions/what-is-dss-dataiku)
- [What is Dataiku DSS used for?](https://www.g2.com/pt/discussions/what-is-dataiku-dss-used-for)

### [IBM watsonx.ai](https://www.g2.com/pt/products/ibm-watsonx-ai/reviews)

Watsonx.ai faz parte da plataforma IBM watsonx que reúne novas capacidades de IA generativa, alimentadas por modelos de base e aprendizado de máquina tradicional em um estúdio poderoso que abrange o ciclo de vida da IA. Com o watsonx.ai, você pode construir, treinar, validar, ajustar e implantar IA generativa, modelos de base e capacidades de aprendizado de máquina com facilidade e construir aplicações de IA em uma fração do tempo com uma fração dos dados.

**Average Rating:** 4.4/5.0

**Total Reviews:** 142

#### How Do G2 Users Rate IBM watsonx.ai?

- **Aplicativo:** 8.8/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 8.5/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 8.6/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.5/10 (Category avg: 8.6/10)

#### Who Is the Company Behind IBM watsonx.ai?

- **Vendedor:** [IBM](https://www.g2.com/pt/sellers/ibm)
- **Website da Empresa:** www.ibm.com
- **Ano de Fundação:** 1911
- **Localização da Sede:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Consultor
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 41% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.ai?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários elogiam a **facilidade de uso** do IBM watsonx.ai, facilitando a integração direta e o desenvolvimento de modelos.
- Os usuários valorizam a **ampla gama de tipos de modelos** no IBM watsonx.ai, aumentando a flexibilidade e a eficiência no desenvolvimento.
- Os usuários apreciam a **plataforma amigável** que simplifica a construção e implantação de modelos de IA de forma eficiente e eficaz.
- Os usuários apreciam o **estúdio de IA amigável** do IBM watsonx.ai, permitindo a criação eficiente de chatbots com codificação mínima.
- Os usuários apreciam a **IA de nível empresarial** do IBM watsonx.ai, que se integra perfeitamente para soluções de negócios práticas e confiáveis.

##### Cons

- Os usuários acham a **curva de aprendizado difícil** desafiadora, indicando a necessidade de documentação mais clara e melhor suporte de integração.
- Os usuários acham a **complexidade** do IBM watsonx.ai desafiadora, especialmente para iniciantes e pequenas equipes que buscam soluções mais fáceis.
- Os usuários acham a **curva de aprendizado íngreme** do IBM watsonx.ai desafiadora, tornando-o menos acessível para equipes não técnicas.
- Os usuários expressam preocupações sobre os **altos custos** do IBM watsonx.ai, achando-o desafiador e não econômico para equipes pequenas.
- Os usuários acham a **configuração complexa** do IBM watsonx.ai desafiadora, especialmente para iniciantes e pequenas equipes que buscam facilidade de uso.

#### What Are Recent G2 Reviews of IBM watsonx.ai?

**["Estúdio de IA Unificado e Governado com Forte Desempenho e Integrações IBM Sem Costura"](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13184421)**

**Rating:** 4.0/5.0 stars

_— Manan S._

[Read full review](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13184421)

**["IBM watsonx.ai Facilita a Integração de Modelos de Fundação em Fluxos de Trabalho Empresariais Reais"](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13271627)**

**Rating:** 4.5/5.0 stars

_— Balaji S._

[Read full review](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13271627)

### [Deepnote](https://www.g2.com/pt/products/deepnote/reviews)

Deepnote é um espaço de trabalho de dados onde agentes e humanos trabalham juntos. É projetado para simplificar a exploração de dados, acelerar a análise e rapidamente entregar insights acionáveis para você e sua equipe. Ao contrário de ferramentas desatualizadas como o Jupyter, o Deepnote é construído com a próxima década em mente. Deepnote dá superpoderes a qualquer pessoa que trabalhe com dados. Ele unifica seu fluxo de trabalho de dados através de uma camada semântica integrada, preparando seus dados para aplicações avançadas de IA. Você também pode aproveitar nosso copiloto de dados de IA para conversar com seus dados, criar gráficos, escrever código ou transformar seus cadernos de IA em dashboards de dados ou aplicativos completos. Combine dados, código SQL ou Python e visualizações lado a lado em uma tela flexível - aprimorada com modelos de raciocínio de IA de ponta. 🤖 Analise com IA • Gere código e visualizações descrevendo seu objetivo. • Escreva, execute e depure código automaticamente com IA. • Avance mais rápido com sugestões de IA sensíveis ao contexto. 🔗 Unifique • Conecte-se a mais de 60 fontes de dados como BigQuery, Snowflake e PostgreSQL. • Combine Python e SQL em um único caderno. • Construa módulos reutilizáveis de ETL, análises e métricas. • Crie uma camada semântica com definições compartilhadas e métricas confiáveis. ⚖️ Escale • Aumente instantaneamente o poder de computação, mais incluído do que o Colab. • Agende tarefas e seja notificado com resultados atualizados. • Organize o trabalho em projetos e pastas para clareza da equipe. • Gerencie fluxos de trabalho via API REST. 🚀 Lançamento • Transforme cadernos em dashboards ou aplicativos de dados, nativamente ou com Streamlit. • Permita que os usuários explorem dados com entradas interativas. • Compartilhe aplicativos seguros e ao vivo com um clique.

**Average Rating:** 4.5/5.0

**Total Reviews:** 382

#### How Do G2 Users Rate Deepnote?

- **Aplicativo:** 8.0/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 7.9/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 7.2/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.8/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Deepnote?

- **Vendedor:** [Deepnote](https://www.g2.com/pt/sellers/deepnote)
- **Website da Empresa:** www.deepnote.com
- **Ano de Fundação:** 2019
- **Localização da Sede:** San Francisco , US
- **Twitter:** @DeepnoteHQ  
5,239 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=0d297cbb8d96f5409e14da076c1669be4aa5b9bc29dd1db4c4a0ea90b79466ed&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdeepnote&secure%5Burl_type%5D=linkedin_company_website)  
17 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Estudante, Analista de Dados
- **Top Industries:** Software de Computador, Educação Superior
- **Company Size:** 67% Small, 25% Medium

#### What Do G2 Reviewers Say About Deepnote?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários acham que a **facilidade de uso** do Deepnote melhora a colaboração e simplifica a análise de dados com sua interface intuitiva.
- Os usuários valorizam as capacidades de **colaboração perfeita** do Deepnote, melhorando o trabalho em equipe e a eficiência em projetos de dados.
- Os usuários valorizam as capacidades de **colaboração em tempo real** do Deepnote, melhorando o trabalho em equipe e a eficiência nos processos analíticos.
- Os usuários valorizam as **integrações fáceis** no Deepnote, permitindo um desenvolvimento mais rápido e uma gestão de dados sem interrupções entre plataformas.
- Os usuários acham **o gerenciamento de dados fácil** com o Deepnote, beneficiando-se de integrações fáceis e capacidades de análise sem interrupções.

##### Cons

- Os usuários percebem **desempenho lento** ao lidar com grandes conjuntos de dados, afetando a velocidade de análise e a experiência do usuário.
- Os usuários acham que os **recursos limitados** do Deepnote restringem sua capacidade de utilizar todo o seu potencial.
- Os usuários enfrentam **problemas de gerenciamento de dados** com desempenho lento e um sistema de gerenciamento de arquivos não intuitivo, afetando a usabilidade.
- Os usuários experimentam **desempenho lento** com o Deepnote, particularmente ao processar grandes conjuntos de dados, afetando a eficiência durante tarefas críticas.
- Os usuários enfrentam **tempos de carregamento lentos** no Deepnote, especialmente com projetos maiores, o que pode prejudicar a produtividade e frustrar as experiências.

#### What Are Recent G2 Reviews of Deepnote?

**["Deepnote torna o trabalho em equipe com dados perfeito com colaboração em tempo real"](https://www.g2.com/pt/survey_responses/deepnote-review-13121723)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/pt/survey_responses/deepnote-review-13121723)

**["Colaboração em Tempo Real que Facilita o Trabalho de Laboratório"](https://www.g2.com/pt/survey_responses/deepnote-review-13100282)**

**Rating:** 5.0/5.0 stars

_— Gabriel P._

[Read full review](https://www.g2.com/pt/survey_responses/deepnote-review-13100282)

#### What Are G2 Users Discussing About Deepnote?

- [How do you use a deep note?](https://www.g2.com/pt/discussions/how-do-you-use-a-deep-note)
- [Is Deepnote open source?](https://www.g2.com/pt/discussions/is-deepnote-open-source)
- [O Deepnote é bom?](https://www.g2.com/pt/discussions/is-deepnote-good) - 1 comment
- [O Deepnote é melhor que o Colab?](https://www.g2.com/pt/discussions/is-deepnote-better-than-colab) - 1 comment

### [Anaconda Core](https://www.g2.com/pt/products/anaconda-core/reviews)

Anaconda é construída para avançar a IA com código aberto em escala, dando aos desenvolvedores e organizações a confiança para aumentar a produtividade, economizar tempo, gastos e riscos associados ao código aberto. 95% das empresas da Fortune 500, incluindo Panasonic, AmTrust, Booz Allen Hamilton e mais de 50 milhões de usuários confiam no valor que a Plataforma Anaconda oferece através de uma abordagem centralizada para obtenção, segurança, construção e implantação de IA. Com 21 bilhões de downloads e crescendo, Anaconda se estabeleceu como o padrão ouro para Python, ciência de dados e IA e a solução pronta para empresas de escolha para inovação em IA. Anaconda está disponível em ambientes híbridos de IA e plataformas de nuvem como AWS, Microsoft Azure, Databricks, Snowflake e mais, com apoio de investidores de classe mundial, incluindo Insight Partners. Saiba mais em https://www.anaconda.com.

**Average Rating:** 4.5/5.0

**Total Reviews:** 234

#### How Do G2 Users Rate Anaconda Core?

- **Aplicativo:** 8.9/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 8.6/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 8.5/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.7/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Anaconda Core?

- **Vendedor:** [Anaconda, Inc.](https://www.g2.com/pt/sellers/anaconda-inc)
- **Ano de Fundação:** 2012
- **Localização da Sede:** Austin, Texas
- **Twitter:** @anacondainc  
83,629 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=525cb36a449061f83743ed7d1f6955ab9666c32bc6b998820814d76556af4f40&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F25029553%2F&secure%5Burl_type%5D=linkedin_company_website)  
580 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Software, Estudante
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 38% Small, 25% Large

#### What Do G2 Reviewers Say About Anaconda Core?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários valorizam a **facilidade de uso** do Anaconda Core, tornando o gerenciamento e a instalação de pacotes simples em todas as plataformas.
- Os usuários acham a **facilidade de configuração** do Anaconda Core notável, tornando a iniciação de projetos e o gerenciamento de pacotes simples e eficiente.
- Os usuários valorizam a **eficiência** do Anaconda Core, aumentando a produtividade e simplificando o fluxo de trabalho para projetos de ciência de dados.
- Os usuários apreciam o **design intuitivo** do Anaconda Core, facilitando o gerenciamento eficiente de projetos e a navegação fácil.
- Os usuários apreciam a **facilidade de codificação** proporcionada pelo Anaconda Core, simplificando o gerenciamento de pacotes e aumentando a produtividade em ciência de dados.

##### Cons

- Os usuários enfrentam **problemas de gerenciamento de dados** com o Anaconda Core, incluindo grandes instalações e desafios com backup e integração.
- Os usuários experimentam **desempenho lento** com o Anaconda Core, particularmente durante a instalação e em hardware mais antigo, afetando a usabilidade.
- Os usuários acham que os **recursos limitados** do Anaconda Core são insuficientes, impactando sua capacidade de utilizar plenamente a plataforma.
- Os usuários acham que os **recursos limitados** do Anaconda Core prejudicam sua experiência e reduzem a funcionalidade em comparação com os concorrentes.
- Os usuários relatam uma preocupação com **armazenamento limitado** , achando o tamanho da instalação do Anaconda Core pesado para seus dispositivos.

#### What Are Recent G2 Reviews of Anaconda Core?

**["Ótimo para Aprender, Precisa de uma Interface Mais Amigável"](https://www.g2.com/pt/survey_responses/anaconda-core-review-12253770)**

**Rating:** 4.0/5.0 stars

_— Firdavs S._

[Read full review](https://www.g2.com/pt/survey_responses/anaconda-core-review-12253770)

**["Kit de Ferramentas Tudo-em-Um para Fluxos de Trabalho de Ciência de Dados"](https://www.g2.com/pt/survey_responses/anaconda-core-review-12706297)**

**Rating:** 4.5/5.0 stars

_— Melissa F._

[Read full review](https://www.g2.com/pt/survey_responses/anaconda-core-review-12706297)

#### What Are G2 Users Discussing About Anaconda Core?

- [O Anaconda é gratuito para empresas?](https://www.g2.com/pt/discussions/anaconda-is-anaconda-free-for-companies) - 2 comments, 1 upvote
- [Is Anaconda free for companies?](https://www.g2.com/pt/discussions/is-anaconda-free-for-companies) - 1 comment, 1 upvote
- [O Anaconda é bom para aprendizado de máquina?](https://www.g2.com/pt/discussions/anaconda-is-anaconda-good-for-machine-learning) - 1 comment, 1 upvote
- [O Anaconda é bom para aprendizado de máquina?](https://www.g2.com/pt/discussions/is-anaconda-good-for-machine-learning) - 1 comment
- [What is Anaconda software used for?](https://www.g2.com/pt/discussions/anaconda-what-is-anaconda-software-used-for)

### [Posit Team](https://www.g2.com/pt/products/posit-team/reviews)

A Posit é uma Public Benefit Corporation que desenvolve software de código aberto e uma plataforma de ciência de dados empresarial. Criamos o RStudio IDE, Shiny, Positron e Quarto — ferramentas usadas por milhões de cientistas de dados, engenheiros de aprendizado de máquina e pesquisadores em todo o mundo, incluindo equipes de 25% das empresas da Fortune Global 100. Nossos produtos comerciais ajudam as organizações a colocar essas ferramentas em produção: o Posit Workbench fornece ambientes de desenvolvimento centralizados que suportam Positron, RStudio, VS Code e Jupyter; o Posit Connect lida com a publicação e implantação para Shiny, aplicações de IA, Streamlit, Dash, FastAPI, Flask, Bokeh e mais; e o Posit Package Manager oferece gerenciamento de pacotes em conformidade com a segurança para R e Python.

**Average Rating:** 4.5/5.0

**Total Reviews:** 568

#### How Do G2 Users Rate Posit Team?

- **Aplicativo:** 8.4/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 8.3/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 8.6/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.3/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Posit Team?

- **Vendedor:** [Posit](https://www.g2.com/pt/sellers/posit)
- **Ano de Fundação:** 2009
- **Localização da Sede:** Boston, US
- **Twitter:** @posit\_pbc  
120,874 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=291e2e1530a4de1dc8ce16ea96948375d8dd3891a186f3101d5f2b110c8b2509&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1978648%2F&secure%5Burl_type%5D=linkedin_company_website)  
442 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Assistente de Pesquisa, Assistente de Pesquisa de Pós-Graduação
- **Top Industries:** Educação Superior, Tecnologia da Informação e Serviços
- **Company Size:** 49% Large, 26% Medium

#### What Do G2 Reviewers Say About Posit Team?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Posit Team, simplificando os fluxos de trabalho de análise de dados e aumentando a produtividade.
- Os usuários elogiam o Posit por seu **desempenho confiável** e **integrações perfeitas** , aumentando a produtividade e simplificando os fluxos de trabalho.
- Os usuários valorizam o **compromisso da Posit com o software de código aberto** , aumentando a produtividade e a integração com a programação em R.
- Os usuários apreciam o **suporte ao cliente responsivo e confiável** da Equipe Posit, melhorando sua experiência geral e produtividade.
- Os usuários apreciam as **integrações fáceis** do Posit Team, melhorando seus fluxos de trabalho com compatibilidade perfeita com várias ferramentas.

##### Cons

- Os usuários experimentam **desempenho lento** com grandes conjuntos de dados, o que interrompe o fluxo de trabalho e exige requisitos de sistema mais altos.
- Os usuários enfrentam uma **curva de aprendizado acentuada** com a Posit Team, tornando o uso inicial e os recursos avançados desafiadores.
- Os usuários relatam **problemas de desempenho** com o Posit Team, particularmente durante o uso com conjuntos de dados maiores e frequentes travamentos.
- Os usuários relatam uma **curva de aprendizado acentuada** com a Posit Team, tornando a configuração inicial e os recursos avançados desafiadores para os novatos.
- Os usuários enfrentam **desempenho lento** com o Posit Team, especialmente ao lidar com grandes conjuntos de dados, impactando a produtividade geral.

#### What Are Recent G2 Reviews of Posit Team?

**["A equipe Posit torna o trabalho bioestatístico reprodutível, colaborativo e seguro."](https://www.g2.com/pt/survey_responses/posit-team-review-12977958)**

**Rating:** 5.0/5.0 stars

_— Donald S._

[Read full review](https://www.g2.com/pt/survey_responses/posit-team-review-12977958)

**["Ferramentas de Ciência de Dados de Código Aberto Excepcionais com Ótima Documentação e Suporte para R/Python"](https://www.g2.com/pt/survey_responses/posit-team-review-13022732)**

**Rating:** 5.0/5.0 stars

_— Omer F. Y._

[Read full review](https://www.g2.com/pt/survey_responses/posit-team-review-13022732)

#### What Are G2 Users Discussing About Posit Team?

- [What is the difference between RStudio desktop and Rstudio server?](https://www.g2.com/pt/discussions/what-is-the-difference-between-rstudio-desktop-and-rstudio-server)
- [What is the difference between R and R studio?](https://www.g2.com/pt/discussions/what-is-the-difference-between-r-and-r-studio)
- [Is R Studio free?](https://www.g2.com/pt/discussions/is-r-studio-free)
- [Qual software é usado para programação em R?](https://www.g2.com/pt/discussions/which-software-is-used-for-r-programming) - 1 comment

### [Alteryx](https://www.g2.com/pt/products/alteryx/reviews)

Alteryx, através da sua plataforma Alteryx One, ajuda as empresas a transformar dados complexos e desconectados em um estado limpo e pronto para IA. Seja criando previsões financeiras, analisando o desempenho de fornecedores, segmentando dados de clientes, analisando a retenção de funcionários ou construindo aplicações de IA competitivas a partir dos seus dados proprietários, o Alteryx One facilita a limpeza, combinação e análise de dados para desbloquear os insights únicos que impulsionam decisões impactantes. Análises Guiadas por IA O Alteryx automatiza e simplifica cada etapa da preparação e análise de dados, desde a validação e enriquecimento até análises preditivas e insights automatizados. Incorpore IA generativa diretamente em seus fluxos de trabalho para agilizar tarefas complexas de dados e gerar insights mais rapidamente. Flexibilidade incomparável, seja você preferir fluxos de trabalho sem código, comandos em linguagem natural ou opções de baixo código, o Alteryx se adapta às suas necessidades. Confiável. Seguro. Pronto para Empresas. O Alteryx é confiado por mais da metade das empresas do Global 2000 e 19 dos 20 maiores bancos globais. Com automação, governança e segurança integradas, seus fluxos de trabalho podem escalar e manter a conformidade enquanto entregam resultados consistentes. E não importa se seus sistemas estão no local, híbridos ou na nuvem; o Alteryx se encaixa perfeitamente na sua infraestrutura. Fácil de Usar. Profundamente Conectado. O que realmente diferencia o Alteryx é nosso foco na eficiência e facilidade de uso para analistas e nossa comunidade ativa de 700.000 usuários do Alteryx para apoiá-lo em cada etapa da sua jornada. Com integração perfeita a dados em todos os lugares, incluindo plataformas como Databricks, Snowflake, AWS, Google, SAP e Salesforce, nossa plataforma ajuda a unificar dados isolados e acelerar a obtenção de insights. Visite Alteryx.com para mais informações e para começar seu teste gratuito.

**Average Rating:** 4.6/5.0

**Total Reviews:** 864

#### How Do G2 Users Rate Alteryx?

- **Aplicativo:** 8.7/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 7.9/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 7.9/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.3/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Alteryx?

- **Vendedor:** [Alteryx](https://www.g2.com/pt/sellers/alteryx)
- **Website da Empresa:** www.alteryx.com
- **Ano de Fundação:** 1997
- **Localização da Sede:** Irvine, CA
- **Twitter:** @alteryx  
26,149 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ae8a7629c5a6d593caff29361a6ee3fb670df11992dd94a9656c66461078b340&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F903031%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,304 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Analista de Dados, Analista
- **Top Industries:** Serviços Financeiros, Contabilidade
- **Company Size:** 63% Large, 21% Medium

#### What Do G2 Reviewers Say About Alteryx?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** no Alteryx, achando simples automatizar tarefas com a funcionalidade de arrastar e soltar.
- Os usuários valorizam as **capacidades de automação** do Alteryx, simplificando processos de dados e aprimorando a eficiência analítica.
- Os usuários acham o Alteryx **muito intuitivo** , tornando-o fácil para usuários não técnicos aprenderem e utilizarem.
- Os usuários acham que a interface do Alteryx torna **o aprendizado de tecnologia fácil** para todos, mesmo para aqueles sem formação em tecnologia.
- Os usuários valorizam o Alteryx por sua **eficiência** em gerenciar dados, simplificar fluxos de trabalho e aumentar a produtividade geral.

##### Cons

- Os usuários destacam o **preço elevado** do Alteryx, tornando difícil para pequenas equipes ou startups arcar com as licenças.
- Os usuários enfrentam uma **curva de aprendizado acentuada** com o Alteryx, exigindo tempo para dominar seus recursos complexos.
- Os usuários acham que o Alteryx sofre de **falta de recursos** , como a ausência de acesso direto ao banco de dados e ferramentas de relatório limitadas.
- Os usuários acham a **dificuldade de aprendizado** do Alteryx acentuada, especialmente para aqueles que não estão familiarizados com RegEx e SQL.
- Os usuários experimentam **desempenho lento** com o Alteryx, particularmente ao lidar com grandes fluxos de trabalho e durante tarefas de manipulação de dados.

#### What Are Recent G2 Reviews of Alteryx?

**["Escala Operações e Economiza Tempo com Fluxos de Trabalho de Dados Automatizados"](https://www.g2.com/pt/survey_responses/alteryx-review-13047637)**

**Rating:** 4.5/5.0 stars

_— Ihor B._

[Read full review](https://www.g2.com/pt/survey_responses/alteryx-review-13047637)

**["Torna a Preparação de Dados Mais Rápida com um Fluxo de Trabalho Intuitivo de Arrastar e Soltar"](https://www.g2.com/pt/survey_responses/alteryx-review-13190742)**

**Rating:** 4.5/5.0 stars

_— Anushka S._

[Read full review](https://www.g2.com/pt/survey_responses/alteryx-review-13190742)

### [Deep Learning Containers](https://www.g2.com/pt/products/deep-learning-containers/reviews)

Os Containers de Deep Learning do Google são imagens Docker pré-configuradas projetadas para simplificar o desenvolvimento e a implantação de modelos de deep learning. Esses containers vêm equipados com frameworks populares de machine learning, como TensorFlow, PyTorch e scikit-learn, juntamente com suas dependências, permitindo que cientistas de dados e desenvolvedores se concentrem no desenvolvimento de modelos sem a complicação da configuração do ambiente. Principais Características e Funcionalidades: - Ambientes Pré-configurados: Cada container inclui frameworks e bibliotecas essenciais de deep learning, garantindo compatibilidade e reduzindo o tempo de configuração. - Escalabilidade: Integração perfeita com os serviços do Google Cloud permite uma escalabilidade eficiente das tarefas de treinamento e inferência. - Flexibilidade: Suporte para vários aceleradores de hardware, incluindo GPUs e TPUs, melhora o desempenho para tarefas computacionalmente intensivas. - Portabilidade: Ambientes consistentes em todas as etapas de desenvolvimento, teste e produção facilitam transições e implantações mais suaves. Valor Principal e Problema Resolvido: Os Containers de Deep Learning abordam as complexidades associadas à configuração e gerenciamento de ambientes de deep learning. Ao fornecer containers otimizados e prontos para uso, eles eliminam a necessidade de instalação e configuração manual de frameworks e dependências de machine learning. Isso acelera o processo de desenvolvimento, garante consistência em diferentes etapas da implantação do modelo e permite que as equipes alocem mais recursos para inovação e refinamento de modelos, em vez de gerenciamento de infraestrutura.

**Average Rating:** 4.7/5.0

**Total Reviews:** 10

#### How Do G2 Users Rate Deep Learning Containers?

- **Aplicativo:** 9.2/10 (Category avg: 8.5/10)
- **Serviço Gerenciado:** 9.2/10 (Category avg: 8.3/10)
- **Compreensão de linguagem natural:** 9.3/10 (Category avg: 8.2/10)
- **Facilidade de administração:** 8.9/10 (Category avg: 8.6/10)

#### Who Is the Company Behind Deep Learning Containers?

- **Vendedor:** [Google](https://www.g2.com/pt/sellers/google)
- **Ano de Fundação:** 1998
- **Localização da Sede:** Mountain View, CA
- **Twitter:** @google  
31,899,995 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 funcionários no LinkedIn®
- **Propriedade:** NASDAQ:GOOG

#### Who Uses This Product?

- **Top Industries:** Tecnologia da Informação e Serviços
- **Company Size:** 50% Large, 30% Medium

#### What Do G2 Reviewers Say About Deep Learning Containers?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam as **integrações fáceis** com frameworks como PyTorch, TensorFlow e Google Cloud Services para fluxos de trabalho sem interrupções.
- Os usuários elogiam a **integração perfeita** dos Contêineres de Aprendizado Profundo com PyTorch, TensorFlow e os Serviços de Nuvem do Google.

##### Cons

- Os usuários acham a **complexidade** dos Contêineres de Deep Learning esmagadora, tornando o uso inicial desafiador e confuso.

#### What Are Recent G2 Reviews of Deep Learning Containers?

**["Contêineres de Aprendizado Profundo Oferecem Ambientes de ML Rápidos e Consistentes no Google Cloud"](https://www.g2.com/pt/survey_responses/deep-learning-containers-review-13184675)**

**Rating:** 4.5/5.0 stars

_— Muhammed A._

[Read full review](https://www.g2.com/pt/survey_responses/deep-learning-containers-review-13184675)

**["Desenvolvimento de IA Rápido e Confiável com Contêineres de Aprendizado Profundo Otimizados para GPU"](https://www.g2.com/pt/survey_responses/deep-learning-containers-review-13172578)**

**Rating:** 4.5/5.0 stars

_— LOKESH G._

[Read full review](https://www.g2.com/pt/survey_responses/deep-learning-containers-review-13172578)

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[Browse Data Science and Machine Learning Platforms Themes](/categories/data-science-and-machine-learning-platforms/themes)

 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 22, 2026

Data science and machine learning (DSML) platforms provide tools to build, deploy, and monitor machine learning (ML) algorithms by combining data with intelligent, decision-making models to support business solutions. These platforms may offer prebuilt algorithms and visual workflows for nontechnical users or require more advanced development skills for complex model creation.

Core capabilities of data science and machine learning (DSML) software

To qualify for inclusion in the Data Science and Machine Learning (DSML) Platforms category, a product must:

- Present a way for developers to connect data to algorithms so they can learn and adapt
- Allow users to create ML algorithms and offer prebuilt algorithms for novice users
- Provide a platform for deploying AI at scale

How DSML software differs from other tools

DSML platforms differ from traditional platform-as-a-service (PaaS) offerings by providing ML–specific functionality, such as prebuilt algorithms, model training workflows, and automated features that reduce the need for extensive data science expertise.

Insights from G2 Reviews on DSML software

According to G2 review data, users highlight the value of streamlined model development, ease of deployment, and options that support both nontechnical and advanced practitioners through visual interfaces or coding-based workflows.

Top Tools at a Glance

| Product | Best for | User Review |
| --- | --- | --- |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_a6c205d533dba77b318af96d91beb2ac/databricks.jpeg "Product Avatar Image")](https://www.g2.com/products/databricks/reviews)[Databricks](https://www.g2.com/products/databricks/reviews)[4.6/5(1,361)](https://www.g2.com/products/databricks/reviews) | Unified lakehouse ML and analytics workflows | "Databricks Streamlines ETL and Analytics with Scalable Notebooks" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_aeae116c52945fdecd7ed16d621cb315/gemini-enterprise-agent-platform.png "Product Avatar Image")](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)[Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)[4.3/5(740)](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) | End-to-end ML lifecycle with GCP-native MLOps | "Easy AI Agent Creation" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_c3e4922bb6835a32854c1dead2cda2bb/sas-sas-viya.jpg "Product Avatar Image")](https://www.g2.com/products/sas-sas-viya/reviews)[SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)[4.3/5(817)](https://www.g2.com/products/sas-sas-viya/reviews) | End-to-end ML lifecycle with governed model deployment | "SAS Viya: Powerful AI & Data Analysis with Seamless Integrations" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2b00e05c107c3273cea5264090c3c1d0/snowflake.jpg "Product Avatar Image")](https://www.g2.com/products/snowflake/reviews)[Snowflake](https://www.g2.com/products/snowflake/reviews)[4.5/5(765)](https://www.g2.com/products/snowflake/reviews) | SQL-native ML pipelines with unified data warehousing | "Snowflake Simplifies Data Management at Scale" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_24bb2b0b5af8e7d875ea09d767bcb097/ibm-watsonx-data.jpg "Product Avatar Image")](https://www.g2.com/products/ibm-watsonx-data/reviews)[IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews)[4.4/5(173)](https://www.g2.com/products/ibm-watsonx-data/reviews) | Unified lakehouse analytics for hybrid AI workloads | "Flexible and Scalable Data Platform for Analytics and AI" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_8972c9f794d61f19e3a284f9592abb5e/hex-tech-hex.png "Product Avatar Image")](https://www.g2.com/products/hex-tech-hex/reviews)[Hex](https://www.g2.com/products/hex-tech-hex/reviews)[4.5/5(404)](https://www.g2.com/products/hex-tech-hex/reviews) | Polyglot SQL-Python notebooks with AI-assisted analysis | "All-in-One Collaborative Workspace for SQL, Python, and Interactive Dashboards" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_d5738f5e7922b4552c3ba543a2b9dee6/matlab.jpeg "Product Avatar Image")](https://www.g2.com/products/matlab/reviews)[MATLAB](https://www.g2.com/products/matlab/reviews)[4.5/5(772)](https://www.g2.com/products/matlab/reviews) | Numerical simulation and ML algorithm prototyping | "A Robust Powerhouse for Advanced Engineering Simulations and Modeling" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_791b528c516cc1b08151fa6da3988161/dataiku.png "Product Avatar Image")](https://www.g2.com/products/dataiku/reviews)[Dataiku](https://www.g2.com/products/dataiku/reviews)[4.4/5(226)](https://www.g2.com/products/dataiku/reviews) | End-to-end ML workflows with no-code/code flexibility | "Build Faster Workflows with Connected Data from many providers or distinct data sources" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_24bb2b0b5af8e7d875ea09d767bcb097/ibm-watsonx-ai.jpg "Product Avatar Image")](https://www.g2.com/products/ibm-watsonx-ai/reviews)[IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)[4.4/5(153)](https://www.g2.com/products/ibm-watsonx-ai/reviews) | Governed end-to-end enterprise AI development | "IBM watsonx.ai Makes It Easy to Bring Foundation Models into Real Business Workflows" |

* * *

Show More

### Data Science and Machine Learning Platforms Topics

- [What are data science and machine learning (DSML) platforms?](#what-are-data-science-and-machine-learning-dsml-platforms)
- [Types of DSML platforms](#types-of-dsml-platforms)
- [What are the common features of data science and machine learning solutions?](#what-are-the-common-features-of-data-science-and-machine-learning-solutions)
- [What are the benefits of using DSML engineering platforms?](#what-are-the-benefits-of-using-dsml-engineering-platforms)
- [Who uses data science and machine learning products?](#who-uses-data-science-and-machine-learning-products)
- [What are the alternatives to data science and machine learning platforms?](#what-are-the-alternatives-to-data-science-and-machine-learning-platforms)
- [Software and services related to data science and machine learning engineering platforms](#software-and-services-related-to-data-science-and-machine-learning-engineering-platforms)
- [Challenges with DSML platforms](#challenges-with-dsml-platforms)
- [Which companies should buy DSML engineering platforms?](#which-companies-should-buy-dsml-engineering-platforms)
- [How to choose the best data science and machine learning (DSML) platform](#how-to-choose-the-best-data-science-and-machine-learning-dsml-platform)
- [Cost of data science and machine learning platforms](#cost-of-data-science-and-machine-learning-platforms)
- [Implementation of data science and machine learning platforms](#implementation-of-data-science-and-machine-learning-platforms)
- [Data science and machine learning platforms trends](#data-science-and-machine-learning-platforms-trends)

[
### Data Science and Machine Learning Platforms Topics
 Expand/Collapse ](#)
- [What are data science and machine learning (DSML) platforms?](#what-are-data-science-and-machine-learning-dsml-platforms)
- [Types of DSML platforms](#types-of-dsml-platforms)
- [What are the common features of data science and machine learning solutions?](#what-are-the-common-features-of-data-science-and-machine-learning-solutions)
- [What are the benefits of using DSML engineering platforms?](#what-are-the-benefits-of-using-dsml-engineering-platforms)
- [Who uses data science and machine learning products?](#who-uses-data-science-and-machine-learning-products)
- [What are the alternatives to data science and machine learning platforms?](#what-are-the-alternatives-to-data-science-and-machine-learning-platforms)
- [Software and services related to data science and machine learning engineering platforms](#software-and-services-related-to-data-science-and-machine-learning-engineering-platforms)
- [Challenges with DSML platforms](#challenges-with-dsml-platforms)
- [Which companies should buy DSML engineering platforms?](#which-companies-should-buy-dsml-engineering-platforms)
- [How to choose the best data science and machine learning (DSML) platform](#how-to-choose-the-best-data-science-and-machine-learning-dsml-platform)
- [Cost of data science and machine learning platforms](#cost-of-data-science-and-machine-learning-platforms)
- [Implementation of data science and machine learning platforms](#implementation-of-data-science-and-machine-learning-platforms)
- [Data science and machine learning platforms trends](#data-science-and-machine-learning-platforms-trends)

## Learn More About Data Science and Machine Learning Platforms

### What are data science and machine learning (DSML) platforms?

The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With data science, of which [artificial intelligence (AI)](https://www.g2.com/articles/what-is-artificial-intelligence) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions.

One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning.

### Types of DSML platforms

Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy [machine learning models](https://www.g2.com/articles/what-is-machine-learning). However, they differ in terms of the data types supported and the method and manner of deployment. 

**Cloud**  **data science and machine learning platforms**

With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models that have been deployed.

**On-premises**  **data science and machine learning platforms**

Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as [HIPAA](https://www.g2.com/glossary/hipaa-definition), require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary.

**Edge**  **platforms**

Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of [data centers](https://www.g2.com/glossary/data-center-definition) that process and store data locally before being sent to a centralized storage center or cloud. [Edge computing](https://learn.g2.com/trends/edge-computing) optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** **

### What are the common features of data science and machine learning solutions?

The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models.

**Data preparation:** Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices.

Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for [data cleansing](https://www.g2.com/articles/data-cleaning) and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start.

**Model training:** Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data.

Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are [supervised learning and unsupervised learning](https://www.g2.com/articles/supervised-vs-unsupervised-learning). The former is a method in which the input is labeled, whereas the latter deals with unlabeled data.

**Model management:** The process does not end once the model is released. Businesses must monitor and manage their models to ensure that they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss.

**Model deployment:** The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more.

### What are the benefits of using DSML engineering platforms?

Through the use of data science and machine learning platforms, data scientists can gain visibility into the entire data journey, from ingestion to inference. This helps them better understand what is and isn’t working and provides them with the tools necessary to fix problems if and when they arise. With these tools, experts prepare and enrich their data, leverage machine learning libraries, and deploy their algorithms into production.

**Share data insights:** Users can share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork.

**Simplify and scale data science:** Many platforms are opening up these tools to a broader audience with easy-to-use features and drag-and-drop capabilities. In addition, pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms easily help scale up experiments across many nodes to perform distributed training on large datasets.

**Experimentation:** Before a model is pushed to production, data scientists spend a significant amount of time working with the data and experimenting to find an optimal solution. Data science and machine learning vendors facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for [deep learning](https://www.g2.com/articles/deep-learning), which are algorithms or methods used to change the attributes of neural networks, such as weights and learning rate, to reduce losses, are also used in experimentation.

### Who uses data science and machine learning products?

Data scientists are in high demand, but skilled professionals are in shortage. The skillset is varied and vast (for example, there is a need to understand various algorithms, advanced mathematics, programming skills, and more). Therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms increasingly include features that make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms.

In addition, for data science projects to initiate, it is key that the broader business buys into them. The more robust platforms provide resources that help nontechnical users understand the models, the data involved, and the aspects of the business that have been impacted.

**Data engineers:** With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization.

**Citizen data scientists:** With the rise of more user-friendly features, citizen data scientists, who are not professionally trained but have developed data skills, are increasingly turning to data science and machine learning platforms to bring AI into their organizations.

**Professional data scientists:** Expert data scientists use these solutions to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment and speeding up data exploration and preparation, as well as model development and training.

**Business stakeholders:** Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations.

### What are the alternatives to data science and machine learning platforms?

Alternatives to data science and machine learning solutions can replace this type of software, either partially or completely:

[AI & machine learning operationalization software](https://www.g2.com/categories/ai-machine-learning-operationalization) **:** Depending on the use case, businesses might consider AI and machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models.

[Machine learning software](https://www.g2.com/categories/machine-learning) **:** Data science and machine learning platforms are great for the full-scale development of models, whether that be for [computer vision](https://learn.g2.com/computer-vision), natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less setup time and development costs.

There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific ones, such as association rule learning, [Bayesian networks](https://www.g2.com/articles/artificial-intelligence-terms#:~:text=Bayesian%20network%3A%20also%20known%20as%20the%20Bayes%20network%2C%20Bayes%20model%2C%20belief%20network%2C%20and%20decision%20network%2C%20is%20a%20graph%2Dbased%20model%20representing%20a%20set%20of%20variables%20and%20their%20dependencies.%C2%A0), clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations look for point solutions.

### **Software and services related to data science and machine learning engineering platforms**

Related solutions that can be used together with DSML platforms include:

[Data preparation software](https://www.g2.com/categories/data-preparation) **:** Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although data science and machine learning platforms offer data preparation features, businesses might opt for a dedicated preparation tool.

[Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have many disparate data sources, and to best integrate all their data, they implement a data warehouse. Data warehouses house data from multiple databases and business applications, which allows business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data ingested by data science and machine learning platforms.

[Data labeling software](https://www.g2.com/categories/data-labeling) **:** To achieve supervised learning off the ground, it is key to have labeled data. Putting in place a systematic, sustained labeling effort can be aided by data labeling software, which provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding AI algorithms.

[Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** [NLP](https://www.g2.com/articles/natural-language-processing) allows applications to interact with human language using a deep learning algorithm. NLP algorithms input language and give a variety of outputs based on the learned task. NLP algorithms provide [voice recognition](https://www.g2.com/articles/voice-recognition) and [natural language generation (NLG)](https://www.g2.com/categories/natural-language-generation-nlg), which converts data into understandable human language. Some examples of NLP uses include [chatbots](https://www.g2.com/categories/chatbots), translation applications, and [social media monitoring tools](https://www.g2.com/categories/social-media-listening-tools) that scan social media networks for mentions.

### Challenges with DSML platforms

Software solutions can come with their own set of challenges. 

**Data requirements:** A great deal of data is required for most AI algorithms to learn what is needed. Users need to train machine learning algorithms using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application.

**Skill shortage:** There is also a shortage of people who understand how to build these algorithms and train them to perform the necessary actions. The common user cannot simply fire up AI software and have it solve all their problems.

**Algorithmic bias:** Although the technology is efficient, it is not always effective and is marred by various types of biases in the training data, such as race or gender biases. For example, since many facial recognition algorithms are trained on datasets with primarily white male faces, others are more likely to be falsely identified by the systems.

### Which companies should buy DSML engineering platforms?

The implementation of AI can have a positive impact on businesses across a host of different industries. Here are a handful of examples:

**Financial services:** AI is widely used in financial services, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With data science and machine learning software solutions, data science teams can build models with company data and deploy them to internal and external applications.

**Healthcare:** Within healthcare, businesses can use these platforms to better understand patient populations, such as predicting in-patient visits and developing systems that can match people with relevant clinical trials. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using data science to speed up the process, using data from past trials, research papers, and more.

**Retail:** In retail, especially e-commerce, personalization rules supreme. The top retailers are leveraging these platforms to provide customers with highly personalized experiences based on factors such as previous behavior and location. With machine learning in place, these businesses can display highly relevant material and catch the attention of potential customers. 

### How to choose the best data science and machine learning (DSML) platform

#### Requirements gathering (RFI/RFP) for DSML platforms

If a company is just starting out and looking to purchase its first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option.

The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format as well as volume. If the company has amassed a lot of data, it needs to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from a data science platform might be helpful.

#### Compare DSML products

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

To ensure a thorough comparison, the user should demo each solution on the short list using the same use case and datasets. This will allow the business to evaluate like-for-like and see how each vendor compares against the competition.

#### Selection of DSML platforms

**Choose a selection team**

Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interests, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants, multitasking, and taking on more responsibilities.

**Negotiation**

Just because something is written on a company’s pricing page does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or to recommend the product to others.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

### Cost of data science and machine learning platforms

As mentioned above, data science and machine learning platforms are available as both on-premises and cloud solutions. Pricing between the two might differ, with the former often requiring more upfront infrastructure costs. 

As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will frequently not have as many features and may have usage caps. DSML vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle.

Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.

#### Return on Investment (ROI)

Businesses decide to deploy data science and machine learning platforms with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.

### Implementation of data science and machine learning platforms

**How are DSML software tools implemented?**

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

**Who is responsible for DSML platform implementation?**

It may require many people or teams to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together its data and begin the journey of data science, starting with proper data preparation and management.

**What is the implementation process for data science and machine learning products?**

In terms of implementation, it is typical for the platform to be deployed in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test its use of a personalization algorithm for a limited number of visitors to its site to understand better how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business).

If the deployment is unsuccessful, the team can return to the drawing board to determine what went wrong. This will involve examining the training data and algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in predictions), the business might need to go back to basics and review their data.

**When should you implement DSML tools?**

As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must make getting their data in order their top priority, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output. 

### Data science and machine learning platforms trends

**AutoML**

AutoML helps automate many tasks needed to develop AI and machine learning applications. Uses include automatic data preparation, automated feature engineering, providing explainability for models, and more.

**Embedded AI**

Machine and deep learning functionality is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it. Using embedded AI inside software like [CRM](https://www.g2.com/categories/crm), [marketing automation](https://www.g2.com/categories/marketing-automation), and [analytics solutions](https://www.g2.com/categories/analytics-tools-software) allows us to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI may gradually pick up in the coming years and may do so in the same way cloud deployment and mobile capabilities have over the past decade. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected.

**Machine learning as a service (MLaaS)**

The software environment has moved to a more granular microservices structure, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as the same companies provide [MLaaS](https://www.g2.com/articles/machine-learning-as-a-service) for other enterprises.

Developers quickly take advantage of these prebuilt algorithms and solutions by feeding them their data to gain insights. Using systems built by enterprise companies helps small businesses save time, resources, and money by eliminating the need to hire skilled machine learning developers. MLaaS will grow further as companies continue to rely on these microservices and the need for AI increases.

**Explainability**

When it comes to machine learning algorithms, especially deep learning, it may be difficult to explain how they arrived at certain conclusions. Explainable AI, also known as XAI, is the process whereby the decision-making process of algorithms is made transparent and understandable to humans. Transparency is the most prevalent principle in the current AI ethics literature, and hence explainability, a subset of transparency, becomes crucial. Data science and machine learning platforms are increasingly including tools for explainability, which helps users build explainability into their models and help them meet data explainability requirements in legislation such as the European Union's privacy law and the GDPR.