# Best Enterprise Data Science and Machine Learning Platforms

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

**Total Products under this Category:** 1,043

### 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
- 14,000+ Authentic Reviews
- 1,043+ 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=1327283&focus%5B%5D=21469&focus%5B%5D=7150&focus%5B%5D=989&focus%5B%5D=1308795&focus%5B%5D=24457&focus%5B%5D=52213)

Highlighted products: Databricks, SAS Viya, Gemini Enterprise Agent Platform, Dataiku, Alteryx, IBM watsonx.ai, MATLAB, and Azure Machine Learning.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=sas-sas-viya&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=dataiku&focus%5B%5D=alteryx&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=matlab&focus%5B%5D=microsoft-azure-machine-learning&segment=enterprise)

**Sponsored**

### Gemini Enterprise Agent Platform

Google Cloud's comprehensive platform for developers to build, scale, govern and optimize agents and models. It's a single destination for technical teams to build agents that can transform enterprise applications and workflows into powerful agentic systems.

[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=2026-08-02T10%3A52%3A01Z&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=21469&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%2Fenterprise%3Fopen_modal_url%3D%252Fproducts%252Fibm-watsonx-ai%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fdata-science-and-machine-learning-platforms%25252Fenterprise%2526source%253Dcategory&secure%5Btoken%5D=5dfaf501b7af74e2b9c4c2c8a2ba2cc2a12943e590e83d58b0503d36af92a0e7&secure%5Burl%5D=https%3A%2F%2Fcloud.google.com%2Fproducts%2Fgemini-enterprise-agent-platform%3Futm_source%3DG2%26utm_medium%3Ddisplay%26utm_campaign%3DCloud-SS-DR-GCP-1713658-GCP-DR-NA-US-en-G2-Display-Banner-All-%2525epid%21-%2525ecid%21-geap%26utm_content%3D%257Bdevice%257D-%257Badgroupid%257D-%257Bnetwork%257D-%257Btargetid%257D-%257Bloc_physical_ms%257D-%257Bcampaignid%257D&secure%5Burl_type%5D=custom_url)

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

Databricks es una plataforma unificada de datos e inteligencia artificial que ayuda a las organizaciones a construir, gobernar y escalar canalizaciones de datos, análisis, aprendizaje automático, aplicaciones de IA y agentes. Más de 20,000 organizaciones en todo el mundo, incluidas adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever y el 70% de las empresas Fortune 500, confían en Databricks para trabajar con datos empresariales e inteligencia artificial a gran escala. Con sede en San Francisco y más de 30 oficinas en todo el mundo, Databricks ofrece una plataforma unificada que incluye Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie y Unity Catalog. Fundada en 2013 por los creadores originales de Apache Spark™, Delta Lake, MLflow y Unity Catalog, Databricks está construida sobre una arquitectura de lakehouse abierta que reúne datos, análisis e inteligencia artificial. La plataforma es utilizada por ingenieros de datos, científicos de datos, analistas, desarrolladores, equipos de aprendizaje automático, equipos de inteligencia artificial y usuarios de negocios para colaborar a lo largo de todo el ciclo de vida de los datos y la inteligencia artificial. Las capacidades clave de Databricks incluyen: - Ingeniería de datos: Construir, automatizar y gestionar canalizaciones de datos por lotes, en streaming y en tiempo real de manera confiable. - Análisis e inteligencia empresarial: Ejecutar análisis SQL, crear paneles de control y permitir que los equipos de negocios exploren datos. - Gobernanza de datos: Descubrir, asegurar y gestionar activos de datos e inteligencia artificial a través de equipos, nubes y cargas de trabajo. - Aprendizaje automático e inteligencia artificial: Desarrollar modelos, construir aplicaciones de inteligencia artificial generativa y crear agentes de inteligencia artificial de grado de producción. - Aplicaciones de datos: Construir y desplegar aplicaciones impulsadas por datos utilizando datos empresariales gobernados. Disponible en AWS, Azure y Google Cloud, Databricks ayuda a las organizaciones a trabajar a través de nubes, reducir silos de datos y simplificar la colaboración entre equipos y herramientas. Los clientes utilizan Databricks para casos de uso como personalización del cliente, detección de fraude, mantenimiento predictivo, análisis en tiempo real, ciberseguridad, investigación en salud, gestión de riesgos financieros, optimización de la cadena de suministro y toma de decisiones impulsada por inteligencia artificial. Databricks se utiliza en industrias como servicios financieros, salud y ciencias de la vida, comercio minorista, manufactura, energía y el sector público. Las organizaciones utilizan la plataforma para modernizar la infraestructura de datos, acelerar la adopción de inteligencia artificial y convertir los datos empresariales en valor de negocio.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,327

#### How Do G2 Users Rate Databricks?

- **Aplicación:** 8.7/10 (Category avg: 8.5/10)
- **Servicio Gestionado:** 8.5/10 (Category avg: 8.3/10)
- **Comprensión del lenguaje natural:** 8.4/10 (Category avg: 8.2/10)
- **Facilidad de administración:** 8.4/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Databricks?

- **Vendedor:** [Databricks Inc.](https://www.g2.com/es/sellers/databricks-inc)
- **Sitio web de la empresa:** databricks.com
- **Año de fundación:** 2013
- **Ubicación de la sede:** San Francisco, CA
- **Twitter:** @databricks  
92,269 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Analista de Datos
- **Top Industries:** Tecnología de la información y servicios, Servicios Financieros
- **Company Size:** 48% Large, 38% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios elogian la **facilidad de uso** y las **características completas** de Databricks para aplicaciones de almacenamiento de datos y ML.
- Los usuarios elogian la **facilidad de uso** de Databricks, mejorando su experiencia con interfaces intuitivas y servicios confiables.
- Los usuarios aprecian las **integraciones fluidas** de Databricks con AWS y otras herramientas, mejorando las operaciones diarias y la eficiencia.
- Los usuarios valoran la **colaboración sin fisuras** que ofrece Databricks, mejorando el trabajo en equipo en proyectos de datos con información en tiempo real.
- Los usuarios elogian las **funciones analíticas integradas** de Databricks, mejorando el procesamiento colaborativo de datos y la visualización de información.

##### Cons

- Los usuarios notan una **curva de aprendizaje pronunciada** inicialmente, con permisos confusos y modos de cómputo que afectan la usabilidad.
- Los usuarios señalan que los **costos pueden ser bastante altos** para utilizar Databricks de manera efectiva, especialmente para proyectos de datos grandes.
- Los usuarios encuentran una **curva de aprendizaje pronunciada** con Databricks, especialmente desafiante para los recién llegados a las herramientas de big data.
- Los usuarios encuentran la **complejidad** de Databricks desafiante, especialmente para equipos más pequeños y procesos de configuración inicial.
- Los usuarios enfrentan **desafíos complejos de configuración** inicialmente, aunque el soporte ayuda a simplificar la experiencia con el tiempo.

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

**["Útil para gestionar y analizar datos operativos"](https://www.g2.com/es/survey_responses/databricks-review-13090803)**

**Rating:** 4.5/5.0 stars

_— Vishaka C._

[Read full review](https://www.g2.com/es/survey_responses/databricks-review-13090803)

**["Databricks simplifica ETL y análisis con cuadernos escalables"](https://www.g2.com/es/survey_responses/databricks-review-13181721)**

**Rating:** 5.0/5.0 stars

_— Diana C._

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

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

- [What does Databricks software do?](https://www.g2.com/es/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote
- [¿Qué es la plataforma de análisis unificada de Databricks?](https://www.g2.com/es/discussions/what-is-databricks-unified-analytics-platform) - 3 comments
- [¿Qué es Lakehouse en Databricks?](https://www.g2.com/es/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes
- [¿Cuáles son las características de Databricks?](https://www.g2.com/es/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes

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

SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.

**Average Rating:** 4.3/5.0

**Total Reviews:** 774

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

- **Application:** 7.8/10 (Category avg: 8.5/10)
- **Managed Service:** 7.9/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 7.8/10 (Category avg: 8.2/10)
- **Ease of Admin:** 7.6/10 (Category avg: 8.5/10)

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

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Company Website:** www.sas.com
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware  
60,863 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Biostatistician
- **Top Industries:** Pharmaceuticals, Banking
- **Company Size:** 33% Large, 33% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** of SAS Viya, which simplifies data visualization and enhances decision-making efficiency.
- Users value the **sophisticated analytical capabilities** of SAS Viya, enabling easy deployment and real-time decision-making.
- Users appreciate the **advanced analytical methods** offered by SAS Viya, enhancing decision-making and logistical data analysis capabilities.
- Users value the **end-to-end data lifecycle tooling** of SAS Viya, enhancing business insight and strategic decision-making.
- Users love the **intuitive interface** of SAS Viya, making data analysis and model deployment effortless for all skill levels.

##### Cons

- Users find SAS Viya to have a **learning difficulty** , making it challenging for non-technical individuals to navigate effectively.
- Users find the **learning curve steep** , making it challenging for non-technical users to navigate SAS Viya effectively.
- Users find the **visualization complexity** in SAS Viya challenging, particularly for non-technical users and beginners.
- Users struggle with the **difficult learning curve** of SAS Viya, particularly for new and non-technical users.
- Users find the **expensive pricing** of SAS Viya to be a significant barrier to entry for potential adoption.

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

**["Effective Data Analysis with SAS Viya"](https://www.g2.com/survey_responses/sas-viya-review-11872818)**

**Rating:** 4.5/5.0 stars

_— Fungai J._

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

**["SAS Viya: Powerful AI & Data Analysis with Seamless Integrations"](https://www.g2.com/survey_responses/sas-viya-review-11855145)**

**Rating:** 5.0/5.0 stars

_— Verified User in Hospital & Health Care_

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

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

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

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

Google Cloud's comprehensive platform for developers to build, scale, govern and optimize agents and models. It's a single destination for technical teams to build agents that can transform enterprise applications and workflows into powerful agentic systems.

**Average Rating:** 4.3/5.0

**Total Reviews:** 654

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

- **Application:** 8.3/10 (Category avg: 8.5/10)
- **Managed Service:** 8.3/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.5/10 (Category avg: 8.2/10)
- **Ease of Admin:** 7.9/10 (Category avg: 8.5/10)

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

- **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?

- **Who Uses This:** Software Engineer, Data Scientist
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 42% Small, 31% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** of Gemini Enterprise Agent Platform, enhancing productivity and streamlining workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity and streamlining machine learning workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity through reduced manual work in projects.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity by streamlining various tasks and processes.
- Users value the **integrated platform** of Gemini, enhancing productivity by combining various functionalities in a unified system.

##### Cons

- Users find the **pricing ambiguous** with unexpected costs, making budget management a challenge on the Gemini platform.
- Users find the platform's **complexity** ,particularly in navigation and advanced features, challenging, especially for beginners.
- The **learning curve is steep** for new users, especially with complex features and pricing transparency issues.
- Users find the **complexity issues** of the Gemini Enterprise Agent Platform lead to high costs and a steep learning curve.
- Users find the **difficult learning** curve of Gemini Enterprise Agent Platform challenging, especially for newcomers to Google Cloud.

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

**["Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)**

**Rating:** 5.0/5.0 stars

_— Danyal A._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)

**["Seamless Google Suite Integration for Everyday Work"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)**

**Rating:** 4.5/5.0 stars

_— Shubham S._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)

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

- [What is Google Cloud AI Platform used for?](https://www.g2.com/discussions/what-is-google-cloud-ai-platform-used-for) - 3 comments, 4 upvotes
- [What software libraries does cloud ML engine support?](https://www.g2.com/discussions/what-software-libraries-does-cloud-ml-engine-support) - 3 comments, 4 upvotes
- [How do I use Google cloud platform for machine learning?](https://www.g2.com/discussions/how-do-i-use-google-cloud-platform-for-machine-learning)
- [Is Google Cloud AI free?](https://www.g2.com/discussions/is-google-cloud-ai-free)
- [What is Google AI platform?](https://www.g2.com/discussions/what-is-google-ai-platform) - 2 comments, 2 upvotes

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

Dataiku es la plataforma para el éxito de la IA: la capa de orquestación de IA donde las empresas construyen, implementan y gobiernan análisis, modelos y agentes a escala. Se sitúa sobre las plataformas de datos, nubes y servicios de IA que ya utilizas, trabajando a través de todos ellos sin encerrarte en ninguno. Dataiku amplía quién puede construir IA de producción, poniendo las herramientas adecuadas en manos de científicos de datos y expertos en el dominio por igual, desde analistas de fraude hasta planificadores de demanda. Orquesta el aprendizaje automático, reglas, LLMs y agentes como un sistema gobernado, construido sobre más de una década de ejecución de IA de producción. La gobernanza es parte de la construcción en lugar de algo añadido después, por lo que los equipos envían más rápido mientras mantienen el rendimiento, el costo y el riesgo bajo control. El resultado: IA que pasa de la experimentación a una ejecución confiable y medible ahora, no en 18 meses.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Dataiku?

- **Aplicación:** 8.3/10 (Category avg: 8.5/10)
- **Servicio Gestionado:** 8.2/10 (Category avg: 8.3/10)
- **Comprensión del lenguaje natural:** 7.8/10 (Category avg: 8.2/10)
- **Facilidad de administración:** 8.0/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Dataiku?

- **Vendedor:** [Dataiku](https://www.g2.com/es/sellers/dataiku)
- **Sitio web de la empresa:** Dataiku.com
- **Año de fundación:** 2013
- **Ubicación de la sede:** New York, NY
- **Twitter:** @dataiku  
22,917 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Científico de Datos, Analista de Datos
- **Top Industries:** Servicios Financieros, Farmacéuticos
- **Company Size:** 60% Large, 22% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian cómo Dataiku facilita el **desarrollo de ML fácil** , permitiendo centrarse en construir modelos sin la complejidad.
- A los usuarios les encanta la **facilidad de uso** en Dataiku, simplificando tareas complejas y mejorando su experiencia de análisis de datos.
- Los usuarios aprecian la **facilidad de uso** en Dataiku, lo que permite la colaboración tanto para usuarios técnicos como no técnicos.
- Los usuarios aprecian las **fáciles integraciones** de Dataiku, facilitando una colaboración y un despliegue fluidos a través de varias herramientas analíticas.
- Los usuarios se benefician de la **mejora de la productividad** de Dataiku, lo que permite un desarrollo de proyectos más rápido y un crecimiento profesional mejorado.

##### Cons

- Los usuarios encuentran la **empinada curva de aprendizaje** de Dataiku desafiante, lo que hace que sea difícil para los principiantes dominar la plataforma.
- Los usuarios encuentran la **curva de aprendizaje pronunciada** desafiante para los principiantes, lo que afecta su capacidad para usar Dataiku de manera efectiva.
- Los usuarios encuentran la **difícil curva de aprendizaje** desafiante, especialmente para los principiantes que navegan por funciones avanzadas.
- Los usuarios experimentan **rendimiento lento** con Dataiku al manejar grandes conjuntos de datos, afectando la eficiencia y la productividad.
- Los usuarios encuentran Dataiku **caro** , especialmente para organizaciones y proyectos más pequeños, lo que afecta la accesibilidad y la asequibilidad.

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

**["Plataforma unificada de bajo código que impulsa la productividad de datos y IA de extremo a extremo"](https://www.g2.com/es/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

**["Construye flujos de trabajo más rápidos con datos conectados de muchos proveedores o fuentes de datos distintas."](https://www.g2.com/es/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

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

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

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

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

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.

**Average Rating:** 4.6/5.0

**Total Reviews:** 856

#### How Do G2 Users Rate Alteryx?

- **Application:** 8.7/10 (Category avg: 8.5/10)
- **Managed Service:** 7.9/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 7.9/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Alteryx?

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx  
26,149 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Analyst, Analyst
- **Top Industries:** Financial Services, Accounting
- **Company Size:** 63% Large, 21% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Alteryx, finding it user-friendly and efficient for non-technical users.
- Users appreciate the **automation capabilities** of Alteryx, enhancing speed and efficiency in data preparation and analysis.
- Users love the **intuitive design** of Alteryx, making data management and workflow creation effortless and efficient.
- Users find Alteryx to be **very easy to learn and use** , enhancing their data workflow and automation experience.
- Users appreciate the **efficiency** of Alteryx, enabling quick data processing and streamlined workflows without complex coding.

##### Cons

- Users mention that Alteryx has a **high cost** which can be challenging for small teams and startups.
- Users find a **steep learning curve** for advanced features, making it challenging for beginners to master Alteryx quickly.
- Users point out the **missing features** in Alteryx, such as limited connectors and issues with output flexibility.
- Users find **learning difficulty** in Alteryx due to confusing tools and troubleshooting errors, especially for beginners.
- Users encounter **slow performance** when processing large datasets, impacting efficiency and usability in Alteryx.

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

**["Scales Operations and Saves Time with Automated Data Workflows"](https://www.g2.com/survey_responses/alteryx-review-13047637)**

**Rating:** 4.5/5.0 stars

_— Ihor B._

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

**["Makes Data Prep Faster with an Intuitive Drag-and-Drop Workflow"](https://www.g2.com/survey_responses/alteryx-review-13190742)**

**Rating:** 4.5/5.0 stars

_— Anushka S._

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

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

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx.ai, you can build, train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data.

**Average Rating:** 4.4/5.0

**Total Reviews:** 137

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

- **Application:** 8.8/10 (Category avg: 8.5/10)
- **Managed Service:** 8.5/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.5/10 (Category avg: 8.5/10)

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

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** www.ibm.com
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 41% Small, 32% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** in IBM watsonx.ai, facilitating quicker AI integration and effective management.
- Users appreciate the **model variety** of IBM watsonx.ai, enabling customized training on existing models for enhanced performance.
- Users appreciate the **seamless integration of enterprise-grade AI** in IBM watsonx.ai, enhancing decision-making and workflow efficiency.
- Users appreciate the **enterprise-grade integrated studio** of IBM watsonx.ai for seamless AI training and reliable insights.
- Users value the **enterprise-grade AI integration** of IBM watsonx.ai, enhancing decision-making and business operations efficiently.

##### Cons

- Users find the **difficult learning** curve of IBM watsonx.ai daunting, making it less accessible for newcomers and smaller teams.
- Users find the **complex setup** of IBM watsonx.ai challenging, making it less suitable for small teams and beginners.
- Users find the **steep learning curve** of IBM watsonx.ai challenging, making it less accessible for non-technical teams.
- Users find the product **expensive** and challenging for small teams, citing high costs and complex setup requirements.
- Users find the **complex setup** of IBM watsonx.ai challenging, especially for beginners and small teams.

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

**["Unified, Governed AI Studio with Strong Performance and Seamless IBM Integrations"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13184421)**

**Rating:** 4.0/5.0 stars

_— Manan S._

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

**["Boosts Productivity with Enterprise AI Insights"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13191468)**

**Rating:** 4.5/5.0 stars

_— suyog k._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13191468)

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

MATLAB is a high-level programming and numeric computing environment widely utilized by engineers and scientists for data analysis, algorithm development, and system modeling. It offers a desktop environment optimized for iterative analysis and design processes, coupled with a programming language that directly expresses matrix and array mathematics. The Live Editor feature enables users to create scripts that integrate code, output, and formatted text within an executable notebook. Key Features and Functionality: - Data Analysis: Tools for exploring, modeling, and analyzing data. - Graphics: Functions for visualizing and exploring data through various plots and charts. - Programming: Capabilities to create scripts, functions, and classes for customized workflows. - App Building: Facilities to develop desktop and web applications. - External Language Interfaces: Integration with languages such as Python, C/C++, Fortran, and Java. - Hardware Connectivity: Support for connecting MATLAB to various hardware platforms. - Parallel Computing: Ability to perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and cloud resources. - Deployment: Options to share MATLAB programs and deploy them to enterprise applications, embedded devices, and cloud environments. Primary Value and User Solutions: MATLAB streamlines complex mathematical computations and data analysis tasks, enabling users to develop algorithms and models efficiently. Its comprehensive toolboxes and interactive apps facilitate rapid prototyping and iterative design, reducing development time. The platform's scalability allows for seamless transition from research to production, supporting deployment on various systems without extensive code modifications. By integrating with multiple programming languages and hardware platforms, MATLAB provides a versatile environment that addresses the diverse needs of engineers and scientists across industries.

**Average Rating:** 4.5/5.0

**Total Reviews:** 750

#### How Do G2 Users Rate MATLAB?

- **Application:** 8.6/10 (Category avg: 8.5/10)
- **Managed Service:** 8.3/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.5/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.4/10 (Category avg: 8.5/10)

#### Who Is the Company Behind MATLAB?

- **Seller:** [MathWorks](https://www.g2.com/sellers/mathworks)
- **Year Founded:** 1984
- **HQ Location:** Natick, MA
- **Twitter:** @MATLAB  
105,142 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Graduate Research Assistant
- **Top Industries:** Higher Education, Research
- **Company Size:** 42% Large, 31% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find MATLAB's **ease of use** remarkable, enabling intuitive data analysis, visualization, and application development seamlessly.
- Users admire MATLAB for its **powerful data analysis and visualization tools** , enabling impactful and professional presentation of results.
- Users highlight MATLAB's **excellent visualization tools** , allowing for clear and professional presentations of complex data.
- Users value the **rich variety of toolboxes** in MATLAB, enhancing efficiency and confidence in their engineering tasks.
- Users appreciate MATLAB for its **powerful simulation capabilities** and seamless transition from ideas to practical solutions.

##### Cons

- Users find MATLAB to be **expensive** , particularly due to high licensing costs and additional toolboxes for functionality.
- Users experience **slow performance** with MATLAB, particularly on less powerful machines, impacting their productivity during complex tasks.
- Users note the **high system requirements** of MATLAB, leading to slower performance on less powerful machines.
- Users struggle with **expensive licensing** costs for MATLAB, posing a barrier for individuals and small companies.
- Users experience **lagging performance** during high resource usage, affecting efficiency and requiring improvements for better usability.

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

**["Powerful Math and Visualization Tools That Boost Productivity"](https://www.g2.com/survey_responses/matlab-review-12811316)**

**Rating:** 5.0/5.0 stars

_— Dipendu M._

[Read full review](https://www.g2.com/survey_responses/matlab-review-12811316)

**["A Robust Powerhouse for Advanced Engineering Simulations and Modeling"](https://www.g2.com/survey_responses/matlab-review-12689149)**

**Rating:** 4.0/5.0 stars

_— Wafa M._

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

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

- [What is MATLAB used for?](https://www.g2.com/discussions/what-is-matlab-used-for) - 1 comment
- [Can I use Matlab for free?](https://www.g2.com/discussions/can-i-use-matlab-for-free) - 3 comments
- [What is Matlab written in?](https://www.g2.com/discussions/what-is-matlab-written-in) - 1 comment
- [Is Matlab a programming language or software?](https://www.g2.com/discussions/is-matlab-a-programming-language-or-software) - 1 comment
- [What is Matlab software used for?](https://www.g2.com/discussions/what-is-matlab-software-used-for) - 1 comment

### [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews)

Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently. Key Features and Functionality: - Data Preparation: Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. - Feature Store: Increase agility in shipping your models by making features discoverable and reusable across workspaces. - AI Infrastructure: Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. - Automated Machine Learning: Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. - Responsible AI: Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. - Model Catalog: Discover, fine-tune, and deploy foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere, and more using the model catalog. - Prompt Flow: Design, construct, evaluate, and deploy language model workflows with prompt flow. - Managed Endpoints: Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Primary Value and Solutions Provided: Azure Machine Learning accelerates time to value by streamlining prompt engineering and machine learning model workflows, facilitating faster model development with powerful AI infrastructure. It streamlines operations by enabling reproducible end-to-end pipelines and automating workflows with continuous integration and continuous delivery (CI/CD). The platform ensures confidence in development through unified data and AI governance with built-in security and compliance, allowing compute to run anywhere for hybrid machine learning. Additionally, it promotes responsible AI by providing visibility into models, evaluating language model workflows, and mitigating fairness, biases, and harm with built-in safety systems.

**Average Rating:** 4.3/5.0

**Total Reviews:** 87

#### How Do G2 Users Rate Azure Machine Learning?

- **Application:** 8.8/10 (Category avg: 8.5/10)
- **Managed Service:** 8.9/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.7/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Machine Learning?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 employees on LinkedIn®
- **Ownership:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 40% Large, 33% Small

#### What Do G2 Reviewers Say About Azure Machine Learning?

_AI-generated summary from verified user reviews_

##### Pros

- Users find Azure Machine Learning to be **easy to use** , facilitating seamless data management and model implementation.
- Users appreciate the **scalability and integration** of Azure Machine Learning, enhancing AI deployment across various applications.
- Users appreciate the **excellent customer support** of Azure Machine Learning, with helpful documentation and community assistance available.
- Users appreciate the **ease of use and rich features** of Azure Machine Learning for effective data management.
- Users appreciate the **efficiency** of Azure Machine Learning for launching and monitoring jobs seamlessly, enhancing productivity.

##### Cons

- Users find the **learning curve challenging** , requiring time and effort to navigate the platform's tools effectively.
- Users find Azure Machine Learning's **difficult navigation** frustrating due to its disordered interface and non-intuitive workflows.
- Users find the **user interface disorganized** , leading to confusion and excessive clicking to locate options.
- Users find the **complex interface** of Azure Machine Learning non-intuitive, complicating their workflow and experience.
- Users face a **difficult learning curve** with Azure Machine Learning, especially if they are new to the platform.

#### What Are Recent G2 Reviews of Azure Machine Learning?

**["Cost-Efficient Medical Data Integration Backed by Great Support"](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)**

**Rating:** 5.0/5.0 stars

_— Giridharan U._

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)

**["An Enterprise-Grade Way to Operationalize ML"](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)**

**Rating:** 4.0/5.0 stars

_— Vytas J._

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)

#### What Are G2 Users Discussing About Azure Machine Learning?

- [What is Azure Machine Learning Studio used for?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio-used-for) - 1 comment
- [What type of data analysis is azure machine learning studio intended for?](https://www.g2.com/discussions/what-type-of-data-analysis-is-azure-machine-learning-studio-intended-for)
- [What are the key features of Azure Machine Learning?](https://www.g2.com/discussions/what-are-the-key-features-of-azure-machine-learning)
- [How do I use Microsoft Azure for machine learning?](https://www.g2.com/discussions/how-do-i-use-microsoft-azure-for-machine-learning)
- [What is Azure Machine Learning Studio?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio)

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

IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data\_Trial

**Average Rating:** 4.4/5.0

**Total Reviews:** 166

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

- **Application:** 5.8/10 (Category avg: 8.5/10)
- **Managed Service:** 7.2/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.0/10 (Category avg: 8.2/10)
- **Ease of Admin:** 7.9/10 (Category avg: 8.5/10)

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

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** www.ibm.com
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, CEO
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 33% Small, 32% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of IBM watsonx.data, finding it reliable and efficient for data management tasks.
- Users value the **organized data integration** and intuitive interface of IBM watsonx.data, enhancing efficiency and analytics.
- Users value the **organized and efficient data management** of IBM watsonx.data, enhancing analytics and reporting tasks seamlessly.
- Users value the **seamless data source integration** in IBM watsonx.data, enhancing flexibility and efficiency for diverse projects.
- Users value the **ability to unify data across hybrid environments** , enhancing flexibility and driving informed decision-making.

##### Cons

- Users find the **learning curve steep** , making initial setup and navigation challenging for newcomers to IBM watsonx.data.
- Users find the **complexity** of setting up IBM watsonx.data a barrier, especially for newcomers to IBM technologies.
- Users find the **pricing steep** for IBM watsonx.data, making it less accessible for smaller businesses and projects.
- Users find the **difficult setup** of IBM watsonx.data time-consuming, with a steep learning curve and complex configurations.
- Users find IBM watsonx.data **difficult to navigate** , especially for beginners and those unfamiliar with AI and data analytics.

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

**["Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve"](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)

**["Unified Data Management with Learning Curve"](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742)**

**Rating:** 5.0/5.0 stars

_— Anchal P._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742)

### [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews)

Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management.

**Average Rating:** 4.3/5.0

**Total Reviews:** 54

#### How Do G2 Users Rate Amazon SageMaker?

- **Application:** 8.6/10 (Category avg: 8.5/10)
- **Managed Service:** 9.1/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 9.3/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.4/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Amazon SageMaker?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 33% Large, 33% Medium

#### What Do G2 Reviewers Say About Amazon SageMaker?

_AI-generated summary from verified user reviews_

##### Pros

- Users find Amazon SageMaker's **ease of use** exceptional, enabling quick adaptation and efficient model training with user-friendly features.
- Users appreciate the **seamless AI integration** of Amazon SageMaker, enhancing the efficiency of the machine learning lifecycle.
- Users appreciate the **superior computing power** of Amazon SageMaker, significantly reducing model training time and enhancing efficiency.
- Users value the **exceptional efficiency** of Amazon SageMaker, significantly reducing model training time and streamlining workflows.
- Users commend the **fast processing** capabilities of Amazon SageMaker, significantly reducing model training time and enhancing usability.

##### Cons

- Users find Amazon SageMaker **expensive** , with complex pricing that leads to unexpected costs for training and deployments.
- Users find the **pricing structure complex** and often face high costs with long training jobs and deployments.
- Users find that the **complexity of pricing** in Amazon SageMaker can lead to unexpected costs and confusion.
- Users note a **steep learning curve** with Amazon SageMaker, particularly for those new to AWS services and setups.
- Users experience a **difficult learning curve** during the initial setup of Amazon SageMaker, which can hinder productivity.

#### What Are Recent G2 Reviews of Amazon SageMaker?

**["End-to-End ML Platform That Streamlines the Full Lifecycle"](https://www.g2.com/survey_responses/amazon-sagemaker-review-13180609)**

**Rating:** 4.5/5.0 stars

_— Atharva P._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-13180609)

**["Fully Managed End-to-End ML in AWS with Powerful Distributed Training"](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)**

**Rating:** 4.0/5.0 stars

_— Hem J._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)

#### What Are G2 Users Discussing About Amazon SageMaker?

- [What is Amazon SageMaker used for?](https://www.g2.com/discussions/what-is-amazon-sagemaker-used-for)
- [Is AWS SageMaker good?](https://www.g2.com/discussions/is-aws-sagemaker-good) - 1 upvote
- [Who uses SageMaker?](https://www.g2.com/discussions/who-uses-sagemaker)
- [How do you use Amazon SageMaker?](https://www.g2.com/discussions/how-do-you-use-amazon-sagemaker)
- [What does Amazon SageMaker do?](https://www.g2.com/discussions/what-does-amazon-sagemaker-do)

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

Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

**Average Rating:** 4.5/5.0

**Total Reviews:** 712

#### How Do G2 Users Rate Snowflake?

- **Application:** 9.2/10 (Category avg: 8.5/10)
- **Managed Service:** 9.0/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Snowflake?

- **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc)
- **Company Website:** www.snowflake.com
- **Year Founded:** 2012
- **HQ Location:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 45% Medium, 43% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Snowflake, finding it fast and effective for data sharing and analytics.
- Users value the **reliable features** of Snowflake, enjoying its intuitive interface and seamless data integration for analytics.
- Users find Snowflake's **data management capabilities** excellent for efficiently aggregating and querying across multiple datasets.
- Users admire the **seamless scalability** of Snowflake, effortlessly accommodating work demands and ensuring optimal performance.
- Users appreciate the **fast data analysis** of Snowflake, enabling quick insights without infrastructure worries.

##### Cons

- Users find Snowflake's **high costs** burdensome, especially for small businesses with limited budgets.
- Users find **feature limitations** in Snowflake, such as lack of code blocks and challenge in permissions management.
- Users find that **cost management** requires discipline, as unexpected charges can accumulate quickly without careful monitoring.
- Users find the **cost structure difficult to optimize** , leading to unexpectedly high initial expenses during implementation.
- Users find Snowflake's **limited features** in dynamic scripts and monitoring hinder flexibility and usability.

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

**["Elastic Scaling and Fast Analytics with Snowflake"](https://www.g2.com/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

**["Snowflake Simplifies Data Management at Scale"](https://www.g2.com/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

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

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

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

### [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews)

Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI. Learn more at Teradata.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 356

#### How Do G2 Users Rate Teradata Autonomous Knowledge Platform?

- **Application:** 8.3/10 (Category avg: 8.5/10)
- **Managed Service:** 8.3/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 7.9/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.0/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Teradata Autonomous Knowledge Platform?

- **Seller:** [Teradata Autonomous Knowledge Platform](https://www.g2.com/sellers/teradata-autonomous-knowledge-platform)
- **Year Founded:** 1979
- **HQ Location:** San Diego, CA
- **Twitter:** @Teradata  
93,113 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=06895b9a8db4fa478ba7da480ccd214a14ef698abd028e4642e62f189e82b650&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1466%2F&secure%5Burl_type%5D=linkedin_company_website)  
9,901 employees on LinkedIn®
- **Ownership:** NYSE:TDC

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Software Engineer
- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 69% Large, 22% Medium

#### What Do G2 Reviewers Say About Teradata Autonomous Knowledge Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Users highlight the **extreme performance** of the Teradata Autonomous Knowledge Platform, especially for processing large data volumes efficiently.
- Users value the **high performance query execution** in Teradata, enhancing their business analytics capabilities significantly.
- Users value the **scalability** of Teradata Autonomous Knowledge Platform, enhancing data integration and operational efficiency significantly.
- Users commend the **high performance and speed** of Teradata, efficiently processing large datasets without issues.
- Users value the **fast processing of large datasets** with Teradata, praising its performance and stability during operations.

##### Cons

- Users find the **steep learning curve** of Teradata Autonomous Knowledge Platform challenging, impacting adoption and productivity temporarily.
- Users find the **steep learning curve** of Teradata Autonomous Knowledge Platform challenging, particularly for those lacking technical expertise.
- Users find the **complexity** of Teradata's platform challenging, particularly for non-technical users and new adopters.
- Users express concerns over the **cost management requirements** needed to avoid potential misusage and performance issues.
- Users feel the **high cost** of Teradata Autonomous Knowledge Platform is a significant drawback affecting accessibility.

#### What Are Recent G2 Reviews of Teradata Autonomous Knowledge Platform?

**["Teradata Vantage Fast Query Performance and Strong Analytics for Big Data"](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)**

**Rating:** 5.0/5.0 stars

_— Muzammil M._

[Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)

**["Teradata Vantage Excels at Big Data Processing and Advanced Analytics"](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)**

**Rating:** 4.5/5.0 stars

_— Nijat I._

[Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)

#### What Are G2 Users Discussing About Teradata Autonomous Knowledge Platform?

- [What does Teradata Data Lab do?](https://www.g2.com/discussions/what-does-teradata-data-lab-do)
- [Is Teradata a premiership?](https://www.g2.com/discussions/is-teradata-a-premiership)
- [What is Teradata Vantage?](https://www.g2.com/discussions/what-is-teradata-vantage)
- [How much does Teradata cost?](https://www.g2.com/discussions/how-much-does-teradata-cost)
- [What is Sandbox in Teradata?](https://www.g2.com/discussions/what-is-sandbox-in-teradata)

### [Deep Learning VM Image](https://www.g2.com/products/deep-learning-vm-image/reviews)

Deep Learning VM Images are pre-configured virtual machine images optimized for data science and machine learning tasks. These images come with essential machine learning frameworks and tools pre-installed, enabling users to deploy and scale machine learning models efficiently on Google Cloud's infrastructure. Key Features and Functionality: - Pre-installed Frameworks: Support for TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, catering to various machine learning needs. - Operating System Options: Based on Debian 11 and Ubuntu 22.04, providing flexibility and compatibility with different environments. - Comprehensive Python Environment: Includes Python 3.10 with a suite of libraries such as NumPy, SciPy, Matplotlib, Pandas, NLTK, Pillow, scikit-image, OpenCV, and scikit-learn, facilitating a robust development experience. - JupyterLab Integration: Offers JupyterLab notebook environments for rapid prototyping and interactive development. - GPU Acceleration: Equipped with the latest NVIDIA drivers and packages, including CUDA 11.x and 12.x, CuDNN, and NCCL, to leverage GPU capabilities for accelerated computation. Primary Value and User Solutions: Deep Learning VM Images streamline the setup process for machine learning projects by providing ready-to-use environments with pre-installed frameworks and tools. This reduces the time and effort required for configuration, allowing data scientists and machine learning practitioners to focus on model development and experimentation. The integration with Google Cloud's scalable infrastructure ensures that users can efficiently manage and scale their machine learning workloads, whether they require CPU or GPU resources. Regular updates and community support further enhance the reliability and performance of these VM images, making them a valuable resource for accelerating machine learning initiatives.

**Average Rating:** 4.4/5.0

**Total Reviews:** 51

#### How Do G2 Users Rate Deep Learning VM Image?

- **Application:** 8.8/10 (Category avg: 8.5/10)
- **Managed Service:** 8.4/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.5/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Deep Learning VM Image?

- **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, Computer Software
- **Company Size:** 48% Small, 31% Medium

#### What Do G2 Reviewers Say About Deep Learning VM Image?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **pre-installed ML frameworks and tools** of Deep Learning VM Image, enhancing efficiency in projects.
- Users love the **ease of use** of Deep Learning VM Image, enabling quick deployment and seamless integration.
- Users value the **easy integrations with cloud services** , which streamline deployment and enhance productivity seamlessly.
- Users benefit from the **fast processing** capabilities of Deep Learning VM Image, enhancing efficiency in deep learning projects.
- Users benefit from the **exceptional speed** of Deep Learning VM Image, significantly accelerating data processing and workflow efficiency.

##### Cons

- Users find the **costs can escalate** with frequent usage, leading to concerns about affordability over time.
- Users express concerns about **high costs** associated with usage, which can escalate for larger-scale operations.
- Users face **high computational costs** and latency issues with Deep Learning VM Image, impacting overall performance and expenses.
- Users find the **steep learning curve** challenging, making it difficult for beginners to navigate the Deep Learning VM Image.
- Users report a **steep learning curve** that can overwhelm new users and hinder effective use of Deep Learning VM Image.

#### What Are Recent G2 Reviews of Deep Learning VM Image?

**["Accurate and Fast Language Translation"](https://www.g2.com/survey_responses/deep-learning-vm-image-review-13179848)**

**Rating:** 4.0/5.0 stars

_— Dhananjaya N._

[Read full review](https://www.g2.com/survey_responses/deep-learning-vm-image-review-13179848)

**["Preconfigured GPU AI Environment That Speeds Up Deep Learning on Google Cloud"](https://www.g2.com/survey_responses/deep-learning-vm-image-review-13155676)**

**Rating:** 4.5/5.0 stars

_— LOKESH G._

[Read full review](https://www.g2.com/survey_responses/deep-learning-vm-image-review-13155676)

### [IBM Decision Optimization](https://www.g2.com/products/ibm-decision-optimization/reviews)

IBM Decision Optimization is a family of prescriptive analytics products that combines mathematical and AI techniques to help with business decision-making including operational, tactical and strategic planning and scheduling use cases. The solutions enable business decision-makers to choose the optimal course of action from millions of alternatives when faced with decisions that involve multiple variables, trade-off possibilities and complex constraints. The solution incorporates powerful optimization solvers namely CPLEX Optimizer and CP Optimizer to solve the breadth of optimization problems including mathematical and constraint programming and constraint-based scheduling models. Learn more about this portfolio here https://www.ibm.com/analytics/decision-optimization IBM ILOG CPLEX Optimization Studio is one of the products within the IBM Decision Optimization portfolio. Organizations across industries are using IBM ILOG CPLEX Optimization Studio to drive operational efficiency and generate significant ROI by optimizing planning, scheduling, pricing and other business decisions. The offering provides users the flexibility to develop optimization models either using general programming language APIs like Python, Java, or using Optimization Programming Language (OPL). The powerful CPLEX optimization engines can deliver the power necessary to solve very large, real- world optimization problems at the speed required for today’s interactive decision optimization applications. Learn more about this product here - https://www.ibm.com/products/ilog-cplex-optimization-studio IBM Decision Optimization is also included within Watson Studio Premium for Cloud Pak for Data to enable data science teams to capitalize on the power of prescriptive analytics and build innovative solutions using a combination of techniques like machine learning and optimization. Data science teams can easily demonstrate business value of optimization by leveraging tools like visual dashboards & modeling assistant to quickly build models, test/evaluate multiple scenarios, solve using powerful optimization engines and deploy the models easily . IBM Decision Optimization solutions bring more than 30 years of experience in the field and is a proven optimization technology and organizations across industries are using IBM Decision Optimization solutions to run their mission-critical decision-making applications and have benefited by way of reduction in operating costs, increase in revenue and accelerated time to value.

**Average Rating:** 4.5/5.0

**Total Reviews:** 35

#### How Do G2 Users Rate IBM Decision Optimization?

- **Application:** 8.3/10 (Category avg: 8.5/10)
- **Managed Service:** 8.3/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 7.3/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind IBM Decision Optimization?

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®
- **Ownership:** SWX:IBM

#### Who Uses This Product?

- **Top Industries:** Computer Software, Financial Services
- **Company Size:** 59% Large, 22% Small

#### What Are Recent G2 Reviews of IBM Decision Optimization?

**["In-depth analytics.. for a price."](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8681163)**

**Rating:** 4.5/5.0 stars

_— Andrew M._

[Read full review](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8681163)

**["leveraging data resources"](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8714924)**

**Rating:** 4.5/5.0 stars

_— James C._

[Read full review](https://www.g2.com/survey_responses/ibm-decision-optimization-review-8714924)

#### What Are G2 Users Discussing About IBM Decision Optimization?

- [What is IBM Decision Optimization used for?](https://www.g2.com/discussions/what-is-ibm-decision-optimization-used-for)
- [What is IBM ILOG cplex optimization studio?](https://www.g2.com/discussions/what-is-ibm-ilog-cplex-optimization-studio)
- [Which IBM offering would be considered best for Prescriptive Analytics?](https://www.g2.com/discussions/which-ibm-offering-would-be-considered-best-for-prescriptive-analytics)
- [What is decision optimization?](https://www.g2.com/discussions/what-is-decision-optimization)
- [What is IBM decision optimization?](https://www.g2.com/discussions/what-is-ibm-decision-optimization)

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

Posit is a Public Benefit Corporation building open-source software and an enterprise data science platform. We created the RStudio IDE, Shiny, Positron, and Quarto — tools used by millions of data scientists, machine learning engineers, and researchers worldwide, including teams at 25% of the Fortune Global 100. Our commercial products help organizations put those tools into production: Posit Workbench provides centralized development environments supporting Positron, RStudio, VS Code, and Jupyter; Posit Connect handles publishing and deployment for Shiny, AI applications, Streamlit, Dash, FastAPI, Flask, Bokeh, and more; and Posit Package Manager provides security-compliant package management for R and Python.

**Average Rating:** 4.5/5.0

**Total Reviews:** 567

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

- **Application:** 8.4/10 (Category avg: 8.5/10)
- **Managed Service:** 8.3/10 (Category avg: 8.3/10)
- **Natural Language Understanding:** 8.6/10 (Category avg: 8.2/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)

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

- **Seller:** [Posit](https://www.g2.com/sellers/posit)
- **Year Founded:** 2009
- **HQ Location:** Boston, US
- **Twitter:** @posit\_pbc  
120,874 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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)  
448 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Research Assistant, Graduate Research Assistant
- **Top Industries:** Higher Education, Information Technology and Services
- **Company Size:** 48% Large, 26% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find Posit to be **highly user-friendly** , enabling efficient analysis and simplifying integration with existing tools.
- Users appreciate Posit's **leadership in innovation** and seamless integration, enhancing their productivity and workflow efficiency.
- Users value Posit's **commitment to open source software** , enhancing accessibility and integration for R programming.
- Users value the **responsive customer support** of Posit Team, enhancing their experience with excellent guidance and assistance.
- Users appreciate the **easy integrations** of Posit Team, allowing seamless workflow and reducing setup complications.

##### Cons

- Users experience **slow performance** when handling large datasets, disrupting workflow and requiring significant system resources.
- Users experience a **steep learning curve** with Posit Team, making initial setup and advanced features challenging.
- Users experience **performance issues** with Posit, particularly when handling larger datasets and during high usage.
- Users face a **steep learning curve** with Posit Team, making initial setup and advanced features challenging for newcomers.
- Users experience **lagging performance** with Posit, particularly when handling large datasets, impacting overall productivity.

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

**["Posit Team Makes Biostatistical Work Reproducible, Collaborative, and Secure"](https://www.g2.com/survey_responses/posit-team-review-12977958)**

**Rating:** 5.0/5.0 stars

_— Donald S._

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

**["Exceptional Open-Source Data Science Tools with Great Documentation and R/Python Support"](https://www.g2.com/survey_responses/posit-team-review-13022732)**

**Rating:** 5.0/5.0 stars

_— Omer F. Y._

[Read full review](https://www.g2.com/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/discussions/what-is-the-difference-between-rstudio-desktop-and-rstudio-server)
- [What is the difference between R and R studio?](https://www.g2.com/discussions/what-is-the-difference-between-r-and-r-studio)
- [Is R Studio free?](https://www.g2.com/discussions/is-r-studio-free)
- [Which software is used for R programming?](https://www.g2.com/discussions/which-software-is-used-for-r-programming) - 1 comment

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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 

Products classified in the overall Data Science and Machine Learning Platforms category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Data Science and Machine Learning Platforms to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Data Science and Machine Learning Platforms category.

In addition to qualifying for inclusion in the Data Science and Machine Learning Platforms category, to qualify for inclusion in the Enterprise Business Data Science and Machine Learning Platforms category, a product must have at least 10 reviews left by a reviewer from an enterprise business.

Top Tools at a Glance

| 

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Unified lakehouse ML and analytics workflows

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User Review

"Helpful for Managing and Analyzing Operational Data"

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End-to-end ML lifecycle with GCP-native MLOps

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"Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform"

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End-to-end ML lifecycle with governed model deployment

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"Effective Data Analysis with SAS Viya"

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SQL-native ML pipelines with unified data warehousing

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"Elastic Scaling and Fast Analytics with Snowflake"

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Unified lakehouse analytics for hybrid AI workloads

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"Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve"

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End-to-end ML workflows with no-code/code flexibility

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"Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity"

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Polyglot SQL-Python notebooks with AI-assisted analysis

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"All-in-One Collaborative Workspace for SQL, Python, and Interactive Dashboards"

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Collaborative notebook analytics with multi-source integration

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"Real-Time Collaboration That Makes Lab Work Easy"

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Numerical simulation and ML algorithm prototyping

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"A Robust Powerhouse for Advanced Engineering Simulations and Modeling"

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Show More

* * *

## How Do You Choose the Right Data Science and Machine Learning Platforms?

### What You Should Know 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.