# Best MLOps Platforms

## How Many MLOps Platforms Products Does G2 Track?

**Total Products under this Category:** 262

### Category Stats (Aug 2026)

- **Average Rating:** 4.5/5 (↓0.01 vs Jul 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Arize AI (+1.02%) - Among all products in this category, Arize AI recorded the largest rating increase compared to last month

_Last updated: August 06, 2026_

## How Does G2 Rank MLOps Platforms Products?

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

- 30 Analysts and Data Experts
- 7,700+ Authentic Reviews
- 262+ 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 MLOps Platforms
 ![G2 Grid® for MLOps Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/mlops-platforms/grids.png?focus%5B%5D=10470&focus%5B%5D=21469&focus%5B%5D=1333204&focus%5B%5D=1308795&focus%5B%5D=52115&focus%5B%5D=125020&focus%5B%5D=10938&focus%5B%5D=1327283)

Highlighted products: Databricks, Gemini Enterprise Agent Platform, Microsoft Fabric, IBM watsonx.ai, Amazon SageMaker, Roboflow, Snowflake, and SAS Viya.

Underlying data: [Grid® JSON](https://www.g2.com/categories/mlops-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=microsoft-fabric&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=amazon-sagemaker&focus%5B%5D=roboflow&focus%5B%5D=snowflake&focus%5B%5D=sas-sas-viya)

**Sponsored**

### Workhuman Social Recognition

Workhuman is the world’s #1 employee recognition platform. It pioneered the category 25+ years ago and is the largest provider by far, with 3x the scale of its nearest competitor. Workhuman’s market leadership is validated by a track record of helping over 7 million users at the world's most iconic brands deliver rewards and recognition programs that drive measurable business impact. In fact, Workhuman offers the only ROI Guarantee in the industry, and prides itself on documented customer proof of delivering increased retention, higher eNPS and engagement. Unlike other vendors who outsource their rewards and support, Workhuman is committed to providing our customers with an exceptional end-to-end experience. From recognition to redemption, never outsourced. The AI-native platform's operational core is one of the world’s largest rewards marketplaces which provides a consumer grade user experience with hundreds of thousands of options and locally curated catalogues in over 150 countries. The marketplace features 24/7 human support in dozens of languages, native mobile apps, and boasts a 95%+ redemption and satisfaction rate. Workhuman offers unrivaled in-house customer support, complemented by specialized consulting teams of former HR leaders and data scientists. These experts combine their knowledge with advanced AI and the proprietary Workhuman iQ platform to transform rich recognition data into real-time insights regarding employee skills, culture, and performance. This capability is proprietary and uniquely branded as "Human Intelligence." With enterprise-grade security and seamless, certified integrations with essential tools like Workday, Microsoft Teams, Slack, and Outlook, Workhuman solidifies its position as the best recognition platform for companies of all sizes – from the Fortune 500 to fast-growing mid-market – and the only one proven to drive business impact that matters.

[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=1910&secure%5Bchosen_at%5D=2026-08-08T15%3A13%3A34Z&secure%5Bdisplayable_resource_id%5D=1201&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=retargeted_product&secure%5Bplacement_resource_ids%5D%5B%5D=17634&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=17634&secure%5Bresource_id%5D=1910&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmlops-platforms&secure%5Btoken%5D=0528c0d5f2595c603fc794d010e0562082b75a34b83b1692f0e83cdca5dc2c8b&secure%5Burl%5D=https%3A%2F%2Fwww.workhuman.com%3Futm_source%3Dg2%26utm_medium%3Dcpc%26utm_campaign%3D1211980577375746%26utm_content%3Ddzaxcdq6wm73aqboy2ihv%26utm_term%3D_product-discoverability_product-discoverability_prospecting~null_g2-clicks-employee-recognition-topic_null_6.09.2026&secure%5Burl_type%5D=custom_url)

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

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,334

#### How Do G2 Users Rate Databricks?

- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)
- **Scalability:** 9.0/10 (Category avg: 9.0/10)
- **Metrics:** 8.8/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.8/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Databricks?

- **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc)
- **Company Website:** databricks.com
- **Year Founded:** 2013
- **HQ Location:** San Francisco, CA
- **Twitter:** @databricks  
92,269 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 47% Large, 38% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications.
- Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services.
- Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency.
- Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights.
- Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization.

##### Cons

- Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability.
- Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects.
- Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools.
- Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes.
- Users face **complex setup** challenges initially, though support helps simplify the experience over time.

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

**["Databricks Streamlines ETL and Analytics with Scalable Notebooks"](https://www.g2.com/survey_responses/databricks-review-13181721)**

**Rating:** 5.0/5.0 stars

_— Diana C._

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

**["Reliable Platform for Building Scalable Data Pipelines"](https://www.g2.com/survey_responses/databricks-review-13198355)**

**Rating:** 5.0/5.0 stars

_— aravind k._

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

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

- [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote
- [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments
- [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes
- [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes

### [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:** 727

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

- **Ease of Use:** 8.2/10 (Category avg: 8.8/10)
- **Scalability:** 8.8/10 (Category avg: 9.0/10)
- **Metrics:** 8.2/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10)

#### 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:** 43% Small, 29% 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?

**["Easy AI Agent Creation"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-13193916)**

**Rating:** 4.5/5.0 stars

_— Belhaje A._

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

**["Helped us automate routine work and save hours every week"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-13212825)**

**Rating:** 4.5/5.0 stars

_— Pavan Simhadri D._

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

#### 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) - 4 comments, 5 upvotes
- [What software libraries does cloud ML engine support?](https://www.g2.com/discussions/what-software-libraries-does-cloud-ml-engine-support) - 4 comments, 5 upvotes
- [How do I use Google cloud platform for machine learning?](https://www.g2.com/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) - 3 comments, 3 upvotes

### [Microsoft Fabric](https://www.g2.com/products/microsoft-fabric/reviews)

Microsoft Fabric is a comprehensive, AI-powered data analytics platform that unifies various data management and analysis tools into a single, integrated environment. It combines the capabilities of Microsoft Power BI, Azure Synapse Analytics, and Azure Data Factory, offering a seamless experience for data integration, engineering, warehousing, real-time analytics, data science, and business intelligence. By centralizing these services, Fabric simplifies data management, enhances collaboration, and accelerates the transformation of raw data into actionable insights. Key Features and Functionality: - Unified Data Lake (OneLake): Fabric provides a single, AI-ready data lake that centralizes and curates all business data within a unified, governed hub, ensuring all teams access accurate datasets securely. - AI-Powered Tools: The platform offers AI-enhanced tools tailored for various data projects, enabling teams to innovate faster and derive near real-time insights that drive business impact. - Integrated Analytics Solutions: Fabric encompasses data integration, data engineering, data warehousing, real-time analytics, data science, and business intelligence, all hosted on a lake-centric SaaS solution for simplicity and to maintain a single source of truth. - Built-in Security and Governance: With robust data security, governance, and compliance features, Fabric ensures that data is managed responsibly and in accordance with industry standards. Primary Value and User Solutions: Microsoft Fabric addresses the complexities associated with managing disparate data systems by providing a unified platform that streamlines data workflows. It empowers organizations to harness the full potential of their data, facilitating informed decision-making and fostering innovation. By integrating various data services, Fabric reduces operational overhead, enhances productivity, and supports the development of AI-driven solutions, positioning businesses to thrive in a data-centric landscape.

**Average Rating:** 4.7/5.0

**Total Reviews:** 44

#### How Do G2 Users Rate Microsoft Fabric?

- **Ease of Use:** 9.1/10 (Category avg: 8.8/10)
- **Scalability:** 9.3/10 (Category avg: 9.0/10)
- **Metrics:** 8.9/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 9.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Microsoft Fabric?

- **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?

- **Top Industries:** Information Technology and Services, Insurance
- **Company Size:** 38% Medium, 38% Large

#### What Do G2 Reviewers Say About Microsoft Fabric?

_AI-generated summary from verified user reviews_

##### Pros

- Users find the **ease of use** of Microsoft Fabric exceptional, allowing seamless adoption even without technical experience.
- Users value the **friendly and responsive customer support** that effectively addresses all inquiries and concerns.
- Users find Microsoft Fabric to be **incredibly intuitive** , making data management accessible for everyone in the organization.
- Users find the **easy setup** of Microsoft Fabric makes it accessible for teams with no prior ETL experience.
- Users value the **integration of tools** in Microsoft Fabric, enhancing usability and offering extensive features for efficiency.

##### Cons

- Users struggle with **formula limitations** in Microsoft Fabric, as some formulas differ from their familiar Excel environment.
- Users face a significant **learning curve** with Microsoft Fabric, which can hinder new users' experience and efficiency.
- Users find **Excel formula issues** frustrating, causing delays while adjusting to Microsoft Fabric's different formula logic.
- Users find a **steep learning curve** in Microsoft Fabric, which can complicate usage for newcomers to the platform.
- Users find that **training is required** to adjust to Microsoft Fabric's formula differences from Excel, but support is available.

#### What Are Recent G2 Reviews of Microsoft Fabric?

**["Great platform for data analytics development and workflow management"](https://www.g2.com/survey_responses/microsoft-fabric-review-10981663)**

**Rating:** 4.5/5.0 stars

_— Amr a._

[Read full review](https://www.g2.com/survey_responses/microsoft-fabric-review-10981663)

**["Finally got our data stack in one place, but costs need attention"](https://www.g2.com/survey_responses/microsoft-fabric-review-12740895)**

**Rating:** 4.0/5.0 stars

_— rishabh m._

[Read full review](https://www.g2.com/survey_responses/microsoft-fabric-review-12740895)

### [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:** 141

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

- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)
- **Scalability:** 8.8/10 (Category avg: 9.0/10)
- **Metrics:** 9.1/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.7/10 (Category avg: 8.7/10)

#### 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, 31% 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?

**["Comprehensive One-Stop Platform for Building and Testing AI Workflows"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13196706)**

**Rating:** 4.0/5.0 stars

_— Manish D._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13196706)

**["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)

### [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?

- **Ease of Use:** 8.4/10 (Category avg: 8.8/10)
- **Scalability:** 9.6/10 (Category avg: 9.0/10)
- **Metrics:** 9.4/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.8/10 (Category avg: 8.7/10)

#### 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% Medium, 33% Large

#### 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?

**["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)

**["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)

#### 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)

### [Roboflow](https://www.g2.com/it/products/roboflow/reviews)

Roboflow ha tutto ciò di cui hai bisogno per costruire e distribuire applicazioni di visione artificiale. Oltre 1.000.000 di utenti da aziende di ogni dimensione — dalle startup alle società pubbliche — utilizzano la piattaforma end-to-end dell'azienda per la raccolta, l'organizzazione, l'annotazione, il preprocessing, l'addestramento del modello e la distribuzione di immagini e video. Roboflow fornisce strumenti per ogni fase del ciclo di vita della distribuzione della visione artificiale e si integra con le tue soluzioni esistenti in modo da poter personalizzare la tua pipeline per soddisfare le tue esigenze.

**Average Rating:** 4.7/5.0

**Total Reviews:** 155

#### How Do G2 Users Rate Roboflow?

- **Facilità d'uso:** 9.3/10 (Category avg: 8.8/10)
- **Scalabilità:** 10.0/10 (Category avg: 9.0/10)
- **Metriche:** 10.0/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Roboflow?

- **Venditore:** [Roboflow](https://www.g2.com/it/sellers/roboflow)
- **Anno di Fondazione:** 2019
- **Sede centrale:** Remote, US
- **Twitter:** @roboflow  
13,577 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=660f87d85fdd82e0f1cecfe2354a16103bb5a6f5508134496575ec655201678c&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F36096640&secure%5Burl_type%5D=linkedin_company_website)  
144 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Founder, Researcher
- **Top Industries:** Software per computer, Ricerca
- **Company Size:** 78% Small, 14% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano l' **interfaccia intuitiva** di Roboflow, che consente un'annotazione efficiente e una collaborazione senza interruzioni per i progetti di visione artificiale.
- Gli utenti apprezzano l' **efficienza** di Roboflow, lodando la sua gestione semplificata dei dataset che fa risparmiare tempo e riduce gli errori.
- Gli utenti apprezzano l' **efficienza dell'annotazione** di Roboflow, godendo di una gestione dei dataset semplificata che risparmia tempo e riduce gli errori.
- Gli utenti apprezzano il **processo di etichettatura dei dati facile** in Roboflow, che semplifica l'annotazione e migliora la collaborazione del team.
- Gli utenti apprezzano le **funzionalità potenti e versatili** di Roboflow, migliorando i progetti accademici e i compiti di visione artificiale.

##### Cons

- Gli utenti trovano Roboflow **costoso** , soprattutto per gli studenti, poiché le funzionalità chiave richiedono piani a pagamento per la privacy e la personalizzazione.
- Gli utenti notano una **mancanza di funzionalità** per analisi avanzate e personalizzazione nei piani di livello inferiore in Roboflow.
- Gli utenti trovano **funzionalità limitate** in Roboflow, affrontando sfide come restrizioni delle funzionalità e mancanza di flessibilità nei compiti avanzati.
- Gli utenti riscontrano **problemi di annotazione** con Roboflow, spesso necessitando di ampie regolazioni manuali per precisione ed efficienza.
- Gli utenti trovano la gestione dell' **etichettatura inefficiente** ingombrante, richiedendo un'organizzazione manuale e mancando di scorciatoie per un completamento più agevole delle annotazioni.

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

**["Accelera la nostra ricerca agri-CV"](https://www.g2.com/it/survey_responses/roboflow-review-12692685)**

**Rating:** 5.0/5.0 stars

_— Alexey K._

[Read full review](https://www.g2.com/it/survey_responses/roboflow-review-12692685)

**["Roboflow rende i progetti di visione artificiale facili da costruire, addestrare e distribuire"](https://www.g2.com/it/survey_responses/roboflow-review-12984362)**

**Rating:** 5.0/5.0 stars

_— noah r._

[Read full review](https://www.g2.com/it/survey_responses/roboflow-review-12984362)

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

Snowflake rende l'IA aziendale facile, efficiente e affidabile. Migliaia di aziende in tutto il mondo, comprese centinaia delle più grandi al mondo, utilizzano l'AI Data Cloud di Snowflake per condividere dati, creare applicazioni e alimentare il loro business con l'IA. L'era dell'IA aziendale è qui. Scopri di più su snowflake.com (NYSE: SNOW).

**Average Rating:** 4.5/5.0

**Total Reviews:** 713

#### How Do G2 Users Rate Snowflake?

- **Facilità d'uso:** 9.0/10 (Category avg: 8.8/10)
- **Scalabilità:** 9.4/10 (Category avg: 9.0/10)
- **Metriche:** 8.9/10 (Category avg: 8.7/10)
- **Flessibilità del Framework:** 9.5/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Snowflake?

- **Venditore:** [Snowflake, Inc.](https://www.g2.com/it/sellers/snowflake-inc)
- **Sito web dell'azienda:** www.snowflake.com
- **Anno di Fondazione:** 2012
- **Sede centrale:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Tecnologia dell'informazione e servizi, Software per computer
- **Company Size:** 45% Medium, 43% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano la **facilità d'uso** di Snowflake, trovandolo veloce ed efficace per la condivisione dei dati e l'analisi.
- Gli utenti apprezzano le **funzionalità affidabili** di Snowflake, godendo della sua interfaccia intuitiva e dell'integrazione dati senza soluzione di continuità per l'analisi.
- Gli utenti trovano le **capacità di gestione dei dati** di Snowflake eccellenti per aggregare e interrogare in modo efficiente più set di dati.
- Gli utenti ammirano la **scalabilità senza soluzione di continuità** di Snowflake, che soddisfa senza sforzo le esigenze lavorative e garantisce prestazioni ottimali.
- Gli utenti apprezzano la **rapida analisi dei dati** di Snowflake, che consente di ottenere rapidamente informazioni senza preoccuparsi dell'infrastruttura.

##### Cons

- Gli utenti trovano i **costi elevati** di Snowflake onerosi, soprattutto per le piccole imprese con budget limitati.
- Gli utenti trovano **limitazioni delle funzionalità** in Snowflake, come la mancanza di blocchi di codice e la difficoltà nella gestione dei permessi.
- Gli utenti scoprono che la **gestione dei costi** richiede disciplina, poiché le spese impreviste possono accumularsi rapidamente senza un attento monitoraggio.
- Gli utenti trovano **difficile ottimizzare la struttura dei costi** , portando a spese iniziali inaspettatamente elevate durante l'implementazione.
- Gli utenti trovano che le **funzionalità limitate** di Snowflake negli script dinamici e nel monitoraggio ostacolino la flessibilità e l'usabilità.

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

**["Scalabilità elastica e analisi veloce con Snowflake"](https://www.g2.com/it/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

**["Snowflake semplifica la gestione dei dati su larga scala"](https://www.g2.com/it/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

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

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

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

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

SAS Viya è una piattaforma di dati e AI nativa del cloud che consente ai team di costruire, distribuire e scalare AI spiegabile che guida decisioni fidate e sicure. Unisce l'intero ciclo di vita dei dati e dell'AI e permette ai team di innovare rapidamente bilanciando velocità, automazione e governance per design. Viya unifica la gestione dei dati, l'analisi avanzata e il decisioning in un'unica piattaforma, così le organizzazioni possono passare dall'esperimentazione alla produzione con fiducia, offrendo un impatto aziendale misurabile che è sicuro, spiegabile e scalabile in qualsiasi ambiente. Le capacità chiave necessarie per fornire decisioni fidate includono: • Chiarezza end-to-end attraverso il ciclo di vita dei dati e dell'AI, con tracciabilità integrata, auditabilità e monitoraggio continuo per supportare decisioni difendibili. • Governance per design, che consente una supervisione coerente su dati, modelli e decisioni per ridurre il rischio e accelerare l'adozione. • AI spiegabile su larga scala, in modo che intuizioni e risultati possano essere compresi, convalidati e fidati sia dalle aziende che dai regolatori. • Analisi operativizzata, garantendo che il valore continui oltre la distribuzione attraverso monitoraggio, riaddestramento e gestione del ciclo di vita. • Distribuzione flessibile e nativa del cloud, permettendo alle organizzazioni di iniziare ovunque e scalare ovunque mantenendo il controllo.

**Average Rating:** 4.3/5.0

**Total Reviews:** 774

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

- **Facilità d'uso:** 8.2/10 (Category avg: 8.8/10)
- **Scalabilità:** 8.2/10 (Category avg: 9.0/10)
- **Metriche:** 8.7/10 (Category avg: 8.7/10)
- **Flessibilità del Framework:** 8.5/10 (Category avg: 8.7/10)

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

- **Venditore:** [SAS Institute Inc.](https://www.g2.com/it/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Sito web dell'azienda:** www.sas.com
- **Anno di Fondazione:** 1976
- **Sede centrale:** Cary, NC
- **Twitter:** @SASsoftware  
60,863 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Biostatistico
- **Top Industries:** Prodotti farmaceutici, Bancario
- **Company Size:** 33% Large, 33% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano la **facilità d'uso** di SAS Viya, che semplifica la visualizzazione dei dati e migliora l'efficienza nel prendere decisioni.
- Gli utenti apprezzano le **sofisticate capacità analitiche** di SAS Viya, che consentono un facile deployment e decisioni in tempo reale.
- Gli utenti apprezzano i **metodi analitici avanzati** offerti da SAS Viya, migliorando le capacità di analisi dei dati decisionali e logistici.
- Gli utenti apprezzano gli **strumenti per il ciclo di vita dei dati end-to-end** di SAS Viya, migliorando l'intuizione aziendale e il processo decisionale strategico.
- Gli utenti amano l' **interfaccia intuitiva** di SAS Viya, rendendo l'analisi dei dati e il deployment dei modelli senza sforzo per tutti i livelli di abilità.

##### Cons

- Gli utenti trovano che SAS Viya abbia una **difficoltà di apprendimento** , rendendo difficile per le persone non tecniche navigare efficacemente.
- Gli utenti trovano la **curva di apprendimento ripida** , rendendo difficile per gli utenti non tecnici navigare efficacemente in SAS Viya.
- Gli utenti trovano la **complessità della visualizzazione** in SAS Viya impegnativa, in particolare per gli utenti non tecnici e i principianti.
- Gli utenti lottano con la **difficile curva di apprendimento** di SAS Viya, in particolare per i nuovi utenti e quelli non tecnici.
- Gli utenti trovano il **prezzo elevato** di SAS Viya un ostacolo significativo all'adozione potenziale.

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

**["Analisi dei dati efficace con SAS Viya"](https://www.g2.com/it/survey_responses/sas-viya-review-11872818)**

**Rating:** 4.5/5.0 stars

_— Fungai J._

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

**["SAS Viya: Potente AI e analisi dei dati con integrazioni senza soluzione di continuità"](https://www.g2.com/it/survey_responses/sas-viya-review-11855145)**

**Rating:** 5.0/5.0 stars

_— Utente verificato in Ospedali e assistenza sanitaria_

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

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

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

### [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?

- **Ease of Use:** 8.5/10 (Category avg: 8.8/10)
- **Scalability:** 9.2/10 (Category avg: 9.0/10)
- **Metrics:** 8.3/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 9.2/10 (Category avg: 8.7/10)

#### 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)

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

Dataiku è la piattaforma per il successo dell'IA: il livello di orchestrazione dell'IA dove le imprese costruiscono, implementano e governano analisi, modelli e agenti su larga scala. Si posiziona sopra le piattaforme di dati, i cloud e i servizi di IA che già utilizzi, lavorando su tutti senza vincolarti a nessuno. Dataiku amplia chi può costruire IA di produzione, mettendo gli strumenti giusti nelle mani sia dei data scientist che degli esperti di dominio, dai analisti delle frodi ai pianificatori della domanda. Orchestri machine learning, regole, LLM e agenti come un unico sistema governato, costruito su oltre un decennio di esecuzione di IA di produzione. La governance è parte integrante della costruzione piuttosto che qualcosa aggiunto successivamente, quindi i team spediscono più velocemente mantenendo sotto controllo prestazioni, costi e rischi. Il risultato: IA che passa dalla sperimentazione a un'esecuzione affidabile e misurabile ora, non tra 18 mesi.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Dataiku?

- **Facilità d'uso:** 8.7/10 (Category avg: 8.8/10)
- **Scalabilità:** 9.1/10 (Category avg: 9.0/10)
- **Metriche:** 8.7/10 (Category avg: 8.7/10)
- **Flessibilità del Framework:** 8.6/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Dataiku?

- **Venditore:** [Dataiku](https://www.g2.com/it/sellers/dataiku)
- **Sito web dell'azienda:** Dataiku.com
- **Anno di Fondazione:** 2013
- **Sede centrale:** New York, NY
- **Twitter:** @dataiku  
22,917 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Scientist, Data Analyst
- **Top Industries:** Servizi finanziari, Prodotti farmaceutici
- **Company Size:** 60% Large, 22% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano come Dataiku faciliti lo **sviluppo di ML facile** , permettendo di concentrarsi sulla costruzione di modelli senza la complessità.
- Gli utenti amano la **facilità d'uso** di Dataiku, che semplifica compiti complessi e migliora la loro esperienza di analisi dei dati.
- Gli utenti apprezzano la **facilità d'uso** in Dataiku, consentendo la collaborazione sia per utenti tecnici che non tecnici.
- Gli utenti apprezzano le **facili integrazioni** di Dataiku, facilitando una collaborazione e un'implementazione fluida tra vari strumenti di analisi.
- Gli utenti beneficiano del **miglioramento della produttività** di Dataiku, che consente uno sviluppo più rapido dei progetti e una crescita professionale migliorata.

##### Cons

- Gli utenti trovano la **ripida curva di apprendimento** di Dataiku impegnativa, rendendo difficile per i principianti padroneggiare la piattaforma.
- Gli utenti trovano la **ripida curva di apprendimento** impegnativa per i principianti, influenzando la loro capacità di utilizzare efficacemente Dataiku.
- Gli utenti trovano la **curva di apprendimento difficile** impegnativa, in particolare per i principianti che navigano tra le funzionalità avanzate.
- Gli utenti sperimentano **prestazioni lente** con Dataiku quando gestiscono grandi set di dati, influenzando l'efficienza e la produttività.
- Gli utenti trovano Dataiku **costoso** , soprattutto per le organizzazioni e i progetti più piccoli, influenzando l'accessibilità e l'accessibilità economica.

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

**["Piattaforma unificata e low-code che aumenta la produttività end-to-end dei dati e dell'IA"](https://www.g2.com/it/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

**["Costruisci flussi di lavoro più veloci con dati connessi da molti fornitori o fonti di dati distinte"](https://www.g2.com/it/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

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

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

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

### [Weights & Biases](https://www.g2.com/products/weights-biases/reviews)

Weights & Biases is the AI developer platform to build AI applications and models with confidence. ML engineers and AI developers use W&B Weave and W&B Models to coordinate all LLMops and MLops processes, including evaluating, debugging, training, fine-tuning, and deploying. W&B Weave helps developers evaluate, monitor and iterate on their AI applications to continuously improve quality, latency, cost, and safety. W&B Models boosts experiment speed and team collaboration among ML teams, helping them bring models to production faster while ensuring performance, data reliability, and security. W&B also serves as the system of record for all ML and AI activities.

**Average Rating:** 4.6/5.0

**Total Reviews:** 53

#### How Do G2 Users Rate Weights & Biases?

- **Ease of Use:** 8.7/10 (Category avg: 8.8/10)
- **Scalability:** 8.3/10 (Category avg: 9.0/10)
- **Metrics:** 9.0/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.6/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Weights & Biases?

- **Seller:** [CoreWeave](https://www.g2.com/sellers/coreweave)
- **Year Founded:** 2017
- **HQ Location:** New York, US
- **Twitter:** @CoreWeave  
23,758 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8dc6fb72f750b09440d04ea4332dc85d5858c061be8cb5e2539ff9987d0c7aee&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcoreweave%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,289 employees on LinkedIn®
- **Ownership:** NASDAQ:CRWV

#### Who Uses This Product?

- **Top Industries:** Computer Software, Research
- **Company Size:** 50% Small, 31% Medium

#### What Do G2 Reviewers Say About Weights & Biases?

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **ease of use** of Weights & Biases, simplifying tracking and sharing experiments effortlessly.
- Users praise the **seamless integration** of Weights & Biases with libraries, enhancing collaboration and simplifying experiment management.
- Users value the **easy setup** of Weights & Biases, enhancing productivity and simplifying collaboration across multiple platforms.
- Users appreciate the **fast and experienced customer support** of Weights & Biases, enhancing their overall experience.
- Users appreciate the **customization flexibility** of Weights & Biases for logging parameters and visualizing model comparisons.

##### Cons

- Users are often frustrated by the **insufficient documentation for basic functionalities** in Weights & Biases.
- Users find the **lack of guidance** in documentation frustrating, especially when seeking basic functionalities in Weights & Biases.
- Users find a **lack of tools** for easily discarding non-useful runs, complicating their workflow with Weights & Biases.
- Users desire **additional features** like global normalization settings and better control over window management on reload.
- Users find the **poor documentation** frustrating, especially when seeking basic functionalities of Weights & Biases.

#### What Are Recent G2 Reviews of Weights & Biases?

**["Essential ML Experiment Tracking with Real-Time Metrics and Team Collaboration"](https://www.g2.com/survey_responses/weights-biases-review-13193236)**

**Rating:** 4.5/5.0 stars

_— Muhammed A._

[Read full review](https://www.g2.com/survey_responses/weights-biases-review-13193236)

**["Streamlined ML Experiment Tracking with Rich Visualizations and Team Collaboration"](https://www.g2.com/survey_responses/weights-biases-review-13216765)**

**Rating:** 4.0/5.0 stars

_— Atharva S._

[Read full review](https://www.g2.com/survey_responses/weights-biases-review-13216765)

#### What Are G2 Users Discussing About Weights & Biases?

- [What is Weights & Biases used for?](https://www.g2.com/discussions/what-is-weights-biases-used-for)

### [SuperAnnotate](https://www.g2.com/it/products/superannotate/reviews)

SuperAnnotate colma il divario tra l'innovazione AI all'avanguardia e i dati umani di alta qualità che la alimentano, aiutando i team AI avanzati a costruire modelli più intelligenti. Con una rete globale di migliaia di esperti rigorosamente selezionati, operazioni gestite etiche e scalabili, abbinamento preciso dei talenti e tecnologia appositamente progettata, SuperAnnotate offre piena visibilità del progetto e qualità dei dati senza pari. SuperAnnotate alimenta flussi di lavoro complessi di annotazione, valutazione e apprendimento per rinforzo per costruire, valutare e allineare l'AI di frontiera. Fidato da innovatori come Databricks, IBM e ServiceNow - e supportato da NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises e Play Time VC di Lionel Messi - SuperAnnotate consente ai migliori team AI del mondo di costruire modelli responsabili e all'avanguardia con dati umani.

**Average Rating:** 4.8/5.0

**Total Reviews:** 354

#### How Do G2 Users Rate SuperAnnotate?

- **Facilità d'uso:** 9.5/10 (Category avg: 8.8/10)
- **Scalabilità:** 9.9/10 (Category avg: 9.0/10)
- **Metriche:** 9.7/10 (Category avg: 8.7/10)
- **Flessibilità del Framework:** 9.9/10 (Category avg: 8.7/10)

#### Who Is the Company Behind SuperAnnotate?

- **Venditore:** [SuperAnnotate](https://www.g2.com/it/sellers/superannotate)
- **Sito web dell'azienda:** superannotate.com
- **Anno di Fondazione:** 2018
- **Sede centrale:** San Francisco, CA
- **Twitter:** @superannotate  
720 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ed4d3a394b0ca2eaac6c63041f9bd1bf14ee26538d356cd977a2b0f50c15f4d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F18999422%2F&secure%5Burl_type%5D=linkedin_company_website)  
361 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Allenatore di dati
- **Top Industries:** Tecnologia dell'informazione e servizi, Software per computer
- **Company Size:** 57% Small, 23% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano l' **interfaccia intuitiva** di SuperAnnotate, rendendo i progetti di annotazione su larga scala facili da gestire ed efficienti.
- Gli utenti apprezzano l' **interfaccia intuitiva** di SuperAnnotate, che migliora l'efficienza e l'accuratezza dei loro compiti di annotazione.
- Gli utenti apprezzano l' **efficienza dell'annotazione** di SuperAnnotate, che consente annotazioni rapide, coerenti e di alta qualità su progetti diversi.
- Gli utenti elogiano l' **efficienza** di SuperAnnotate, apprezzando il tempo risparmiato e il processo di annotazione semplificato.
- Gli utenti apprezzano SuperAnnotate per le sue **annotazioni di alta qualità** , garantendo una collaborazione e una gestione dei progetti coerente ed efficiente.

##### Cons

- Gli utenti notano **problemi di prestazioni** con SuperAnnotate, in particolare relativi ai tempi di caricamento e a occasionali problemi tecnici.
- Gli utenti riscontrano **prestazioni lente** con SuperAnnotate, in particolare durante il ritaglio delle immagini e la gestione di progetti di grandi dimensioni.
- Gli utenti trovano la **difficile curva di apprendimento** per le funzionalità avanzate impegnativa, influenzando la loro esperienza complessiva con SuperAnnotate.
- Gli utenti trovano la **complessità della piattaforma** scoraggiante, in particolare per i nuovi utenti che navigano tra le funzionalità avanzate.
- Gli utenti trovano una **mancanza di guida** in SuperAnnotate, rendendo difficile per i nuovi arrivati navigare efficacemente tra le funzionalità avanzate.

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

**["Strumenti di organizzazione dei dati facili da usare e potenti strumenti di divisione delle immagini"](https://www.g2.com/it/survey_responses/superannotate-review-13146852)**

**Rating:** 5.0/5.0 stars

_— Doniaa K._

[Read full review](https://www.g2.com/it/survey_responses/superannotate-review-13146852)

**["Annotazione dei dati semplificata con facilità"](https://www.g2.com/it/survey_responses/superannotate-review-13215367)**

**Rating:** 5.0/5.0 stars

_— EMAN S._

[Read full review](https://www.g2.com/it/survey_responses/superannotate-review-13215367)

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

- [What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?](https://www.g2.com/it/discussions/what-is-your-experience-with-superannotate-for-data-annotation-and-what-would-you-like-to-see-improved) - 1 comment
- [How do I annotate an image in OpenCV?](https://www.g2.com/it/discussions/how-do-i-annotate-an-image-in-opencv)
- [Is SuperAnnotate free?](https://www.g2.com/it/discussions/is-superannotate-free)
- [How do you use SuperAnnotate?](https://www.g2.com/it/discussions/how-do-you-use-superannotate)
- [What is SuperAnnotate?](https://www.g2.com/it/discussions/what-is-superannotate) - 1 comment, 2 upvotes

### [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews)

Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic pipeline generation and seamless integration with various technologies. Airflow's modular architecture and message queue system allow it to scale efficiently, managing workflows from single machines to large-scale distributed systems. Its user-friendly web interface provides comprehensive monitoring and management capabilities, offering clear insights into task statuses and execution logs. Key Features: - Pure Python: Workflows are defined using standard Python code, allowing for dynamic pipeline generation and easy integration with existing Python libraries. - User-Friendly Web Interface: A robust web application enables users to monitor, schedule, and manage workflows without the need for command-line interfaces. - Extensibility: Users can define custom operators and extend libraries to fit their specific environment, enhancing the platform's flexibility. - Scalability: Airflow's modular architecture and use of message queues allow it to orchestrate an arbitrary number of workers, making it ready to scale as needed. - Robust Integrations: The platform offers numerous plug-and-play operators for executing tasks across various cloud platforms and third-party services, facilitating easy integration with existing infrastructure. Primary Value and Problem Solving: Apache Airflow addresses the challenges of managing complex data workflows by providing a scalable and dynamic platform for workflow orchestration. By defining workflows as code, it ensures reproducibility, version control, and collaboration among teams. The platform's extensibility and robust integrations allow organizations to adapt it to their specific needs, reducing operational overhead and improving efficiency in data processing tasks. Its user-friendly interface and monitoring capabilities enhance transparency and control over workflows, leading to improved data quality and reliability.

**Average Rating:** 4.4/5.0

**Total Reviews:** 128

#### How Do G2 Users Rate Apache Airflow?

- **Ease of Use:** 8.5/10 (Category avg: 8.8/10)
- **Scalability:** 9.1/10 (Category avg: 9.0/10)
- **Metrics:** 8.5/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 9.0/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Apache Airflow?

- **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation)
- **Year Founded:** 1999
- **HQ Location:** Wakefield, MA
- **Twitter:** @TheASF  
66,168 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b8484c4bb31e87bc0ba9e683d86f2af14309539343a063507c88bcdcff98434d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F215982%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,470 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 45% Medium, 31% Large

#### What Do G2 Reviewers Say About Apache Airflow?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** in Apache Airflow, facilitating efficient workflow creation and monitoring.
- Users appreciate the **intuitive dashboard** of Apache Airflow for effortlessly monitoring workflows and task statuses.
- Users highly value the **flexibility** of Apache Airflow, allowing for customized workflows via Python code.
- Users appreciate the **workflow automation** capabilities of Apache Airflow, simplifying complex data pipeline management.
- Users appreciate the **easy integrations** in Apache Airflow, making it flexible for connecting various systems and tools.

##### Cons

- Users face a **difficult setup** when installing Apache Airflow, especially on Windows systems, complicating the onboarding process.
- Users find the **learning curve challenging** , requiring time to grasp operators and manage workflows effectively.
- Users find the **steep learning curve** of Airflow challenging, especially with concepts and setup complexities.
- Users find the **learning difficulty** of Apache Airflow to be a barrier, particularly with Jinja and job setups.
- Users find the **outdated user interface** of Apache Airflow detracts from an otherwise reliable experience.

#### What Are Recent G2 Reviews of Apache Airflow?

**["Scalable Workflows with Apache Airflow, Best data Engineering tool for Orchestrator,Easy Deployment"](https://www.g2.com/survey_responses/apache-airflow-review-12703177)**

**Rating:** 4.5/5.0 stars

_— Rajesh K._

[Read full review](https://www.g2.com/survey_responses/apache-airflow-review-12703177)

**["Powerful for complex ML pipelines, but comes with a steep infrastructure learning curve"](https://www.g2.com/survey_responses/apache-airflow-review-12935519)**

**Rating:** 5.0/5.0 stars

_— Sachin G._

[Read full review](https://www.g2.com/survey_responses/apache-airflow-review-12935519)

#### What Are G2 Users Discussing About Apache Airflow?

- [What is Apache Airflow used for?](https://www.g2.com/discussions/what-is-apache-airflow-used-for)
- [What is airflow technology?](https://www.g2.com/discussions/what-is-airflow-technology) - 1 comment
- [Is airflow a framework?](https://www.g2.com/discussions/is-airflow-a-framework) - 1 comment
- [Is Apache airflow an ETL tool?](https://www.g2.com/discussions/is-apache-airflow-an-etl-tool) - 1 comment
- [Who is using Apache airflow?](https://www.g2.com/discussions/who-is-using-apache-airflow) - 1 comment

### [JFrog](https://www.g2.com/it/products/jfrog-2024-03-28/reviews)

JFrog Ltd. (Nasdaq: FROG), i creatori della piattaforma unificata DevOps, DevSecOps, DevGovOps e MLOps, ha la missione di creare un mondo di software consegnato senza attriti dallo sviluppo alla produzione. Guidata da una visione di "Liquid Software" per mantenere il software in flusso continuo, sicuro e sempre aggiornato, la Piattaforma JFrog serve come il sistema di registrazione definitivo della catena di fornitura del software. È progettata in modo unico per potenziare le organizzazioni mentre costruiscono, gestiscono e distribuiscono software affidabile con velocità, sicurezza e scala senza precedenti in ambienti ibridi e multi-cloud. Man mano che l'ingegneria del software evolve nell'era dell'IA, le nuove offerte di JFrog affrontano la tendenza più pressante del settore: l'ascesa dello sviluppo software agentico e i rischi di sicurezza nascosti dell'"AI ombra". In risposta agli attori delle minacce che prendono sempre più di mira i flussi di lavoro degli sviluppatori, inclusa un'enorme ondata di modelli AI open-source dannosi e pacchetti infetti; JFrog ha ampliato le capacità della sua piattaforma per fornire visibilità assoluta end-to-end e conformità automatizzata. Le nuove innovazioni chiave includono il JFrog AI Catalog, che consente alle organizzazioni di centralizzare, governare e controllare il ciclo di vita dei modelli AI approvati per l'uso aziendale. Per proteggere gli ambienti di codifica autonomi, JFrog ha introdotto il Registro Universale MCP e il Registro delle Competenze degli Agenti (sviluppato insieme a NVIDIA). Queste nuove soluzioni stabiliscono il primo strato di fiducia di livello aziendale del settore per gestire e archiviare in sicurezza le competenze degli agenti AI, monitorare le connessioni e bloccare istantaneamente strumenti di sviluppo non sicuri o estensioni di codifica dannose proprio dove lavorano gli sviluppatori. Inoltre, l'integrazione di strumenti avanzati DevGovOps e di Sicurezza Runtime consente ai team di sostituire le lente verifiche di conformità manuali con l'applicazione continua delle politiche in background. Spostando la sicurezza a sinistra direttamente nella pipeline binaria, JFrog garantisce che il volume di codice assistito dall'IA non superi la capacità di un'organizzazione di verificarne la sicurezza. Oggi, milioni di utenti e circa 6.600 organizzazioni in tutto il mondo, inclusa la maggior parte delle Fortune 100, dipendono dalla piattaforma universale JFrog per eliminare la fatica delle soluzioni puntuali, colmare il divario di governance e abbracciare in sicurezza la trasformazione digitale. Scopri di più su www.jfrog.com o seguici su X @JFrog.

**Average Rating:** 4.2/5.0

**Total Reviews:** 150

#### How Do G2 Users Rate JFrog?

- **Facilità d'uso:** 8.1/10 (Category avg: 8.8/10)
- **Scalabilità:** 10.0/10 (Category avg: 9.0/10)

#### Who Is the Company Behind JFrog?

- **Venditore:** [JFrog Ltd](https://www.g2.com/it/sellers/jfrog-ltd)
- **Sito web dell'azienda:** jfrog.com
- **Anno di Fondazione:** 2008
- **Sede centrale:** Sunnyvale, CA
- **Twitter:** @jfrog  
23,186 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9e9f01c1efeb3f3e7b4535b3aefc16344bbb21773bc11bf4ad186f193dbcaabf&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fjfrog-ltd%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,364 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, DevOps Engineer
- **Top Industries:** Tecnologia dell'informazione e servizi, Software per computer
- **Company Size:** 51% Large, 31% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano l' **integrazione completa e il supporto multi-formato** di JFrog, semplificando efficacemente i loro processi DevOps.
- Gli utenti apprezzano la **gestione centralizzata degli artefatti** di JFrog, migliorando l'efficienza nella memorizzazione e nel tracciamento dei componenti attraverso gli ambienti.
- Gli utenti apprezzano l' **integrazione di distribuzione senza soluzione di continuità** di JFrog, migliorando efficacemente le pipeline CI/CD e la gestione della sicurezza.
- Gli utenti apprezzano le **integrazioni senza soluzione di continuità** di JFrog, migliorando i loro processi CI/CD attraverso vari formati di pacchetti.
- Gli utenti apprezzano le **facili integrazioni** di JFrog con vari strumenti, migliorando i loro flussi di lavoro CI/CD senza problemi.

##### Cons

- Gli utenti trovano la piattaforma di JFrog **eccessivamente complessa** , richiedendo un addestramento significativo per navigare efficacemente tra le sue numerose funzionalità.
- Gli utenti trovano JFrog **costoso** , con i costi che rappresentano una sfida per i team più piccoli e gli sviluppatori individuali.
- Gli utenti spesso affrontano una **ripida curva di apprendimento** con JFrog, richiedendo un tempo significativo per padroneggiarne la complessità.
- Gli utenti trovano che la **difficile curva di apprendimento** di JFrog richieda un ampio addestramento per navigare efficacemente nelle sue caratteristiche complesse.
- Gli utenti trovano che JFrog abbia una **curva di apprendimento ripida** , richiedendo tempo e sforzo significativi per raggiungere la competenza.

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

**["JFrog semplifica la gestione degli artefatti per implementazioni organizzate e affidabili"](https://www.g2.com/it/survey_responses/jfrog-review-12870354)**

**Rating:** 4.5/5.0 stars

_— Subhashree S._

[Read full review](https://www.g2.com/it/survey_responses/jfrog-review-12870354)

**["Gestione centralizzata degli artefatti che semplifica CI/CD"](https://www.g2.com/it/survey_responses/jfrog-review-13220023)**

**Rating:** 5.0/5.0 stars

_— Utente verificato in Produzione_

[Read full review](https://www.g2.com/it/survey_responses/jfrog-review-13220023)

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

- [Quali sono i benefici e le sfide dell'utilizzo di JFrog per gestire la tua catena di fornitura software?](https://www.g2.com/it/discussions/what-are-the-benefits-and-challenges-of-using-jfrog-for-managing-your-software-supply-chain)
- [What does Jfrog Platform do?](https://www.g2.com/it/discussions/what-does-jfrog-platform-do)
- [What is difference between JFrog and Nexus?](https://www.g2.com/it/discussions/what-is-difference-between-jfrog-and-nexus)
- [What is Artifactory software used for?](https://www.g2.com/it/discussions/what-is-artifactory-software-used-for)

### [Edge Impulse](https://www.g2.com/pt/products/edge-impulse/reviews)

Edge Impulse é uma plataforma completa para desenvolvimento de aplicações de IA na borda. Nós permitimos que os desenvolvedores usem seus próprios dados de sensores, áudio e visão para treinar modelos de IA para classificação, regressão e detecção de anomalias. Nossa plataforma é consciente de hardware e os desenvolvedores podem construir modelos que escalam de MCUs a NPUs. Nós apoiamos MLOps do início ao fim - desde a coleta inicial de dados até o monitoramento do modelo em campo.

**Average Rating:** 4.5/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate Edge Impulse?

- **Facilidade de Uso:** 8.8/10 (Category avg: 8.8/10)
- **Escalabilidade:** 8.1/10 (Category avg: 9.0/10)
- **Métricas:** 8.3/10 (Category avg: 8.7/10)
- **Flexibilidade de estrutura:** 8.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Edge Impulse?

- **Vendedor:** [Qualcomm](https://www.g2.com/pt/sellers/qualcomm)
- **Ano de Fundação:** 1985
- **Localização da Sede:** San Diego, CA
- **Twitter:** @Qualcomm  
441,209 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=c011c5355b10e30f99e8c597addd4e1f5e9def722397be57a2defdd1a5140c47&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fqualcomm%2F&secure%5Burl_type%5D=linkedin_company_website)  
56,625 funcionários no LinkedIn®
- **Propriedade:** NASDAQ:QCOM

#### Who Uses This Product?

- **Company Size:** 64% Small, 36% Large

#### What Do G2 Reviewers Say About Edge Impulse?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários valorizam as **extensas opções de aumento de dados e implantação** oferecidas pela Edge Impulse para um desempenho aprimorado do modelo.
- Os usuários apreciam a **facilidade de uso** do Edge Impulse, achando sua interface intuitiva para importar e dimensionar dados.
- Os usuários valorizam as **ferramentas de aumento e pré-processamento de dados** que melhoram a qualidade dos dados de treinamento e o desempenho do modelo.
- Os usuários valorizam a **flexibilidade** do Edge Impulse para implantar modelos em diversos dispositivos de borda e formatos.
- Os usuários acham que a **interface amigável** do Edge Impulse torna o aprendizado de máquina acessível para vários dispositivos de borda sem esforço.

##### Cons

- Os usuários sentem que a **falta de documentação offline** pode dificultar o uso do Edge Impulse em áreas de baixa conectividade.
- Os usuários sentem que a **falta de ferramentas** limita o suporte para dispositivos embarcados personalizados, dificultando uma experimentação mais ampla.
- Os usuários acham a **personalização limitada** do Edge Impulse restritiva para construir modelos de aprendizado de máquina complexos ou especializados.
- Os usuários sentem que **falta de recursos** para dispositivos incorporados personalizados limita o apelo e a usabilidade do produto para desenvolvedores.
- Os usuários acham as **limitações do modelo** do Edge Impulse restritivas para aplicações complexas e especializadas, desejando mais opções de personalização.

#### What Are Recent G2 Reviews of Edge Impulse?

**["Usando o Edge como um usuário relativamente novo"](https://www.g2.com/pt/survey_responses/edge-impulse-review-8506732)**

**Rating:** 5.0/5.0 stars

_— Georgian C._

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**["Capacitando a Inovação em IA de Borda: Uma Revisão Abrangente do Edge Impulse"](https://www.g2.com/pt/survey_responses/edge-impulse-review-8506779)**

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_— Alex G._

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[Browse MLOps Platforms Themes](/categories/mlops-platforms/themes)

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

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

Updated April 9, 2026

Machine learning operationalization (MLOps) platforms allow users to manage, monitor, and deploy machine learning models as they are integrated into business applications, automating deployment, tracking model health and accuracy, and enabling teams to scale machine learning across the organization for tangible business impact.

### Core Capabilities of MLOps Platforms

To qualify for inclusion in the MLOps Platforms category, a product must:

- Offer a platform to monitor and manage machine learning models
- Allow users to integrate models into business applications across a company
- Track the health and performance of deployed machine learning models
- Provide a holistic management tool to better understand all models deployed across a business

### Common Use Cases for MLOps Platforms

Data science and ML engineering teams use MLOps platforms to operationalize models and maintain their performance over time. Common use cases include:

- Automating the deployment pipeline for ML models built by data scientists into production applications
- Monitoring model drift, accuracy degradation, and performance anomalies in deployed models
- Managing experiment tracking, model versioning, and security governance across the ML lifecycle

### How MLOps Platforms Differ from Other Tools

MLOps platforms focus on the maintenance and monitoring of deployed models rather than initial model development, distinguishing them from [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), which focus on model building and training. Some MLOps solutions offer centralized management of all models across the business in a single location, and may be language-agnostic or optimized for specific languages like Python or R.

### Insights from G2 on MLOps Platforms

Based on category trends on G2, model monitoring and experiment tracking stand out as the most valued capabilities. Improved model reliability and faster iteration cycles stand out as primary benefits of adoption.

Top Tools at a Glance

| Product | Best for | User Review |
| --- | --- | --- |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_a6c205d533dba77b318af96d91beb2ac/databricks.jpeg "Product Avatar Image")](https://www.g2.com/products/databricks/reviews)[Databricks](https://www.g2.com/products/databricks/reviews)[4.6/5(1,363)](https://www.g2.com/products/databricks/reviews) | Unified lakehouse for ML and data engineering | "Reliable Platform for Building Scalable Data Pipelines" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_aeae116c52945fdecd7ed16d621cb315/gemini-enterprise-agent-platform.png "Product Avatar Image")](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)[Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)[4.3/5(745)](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) | End-to-end ML lifecycle on Google Cloud | "Easy AI Agent Creation" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_10e5841519b4608dd7454b21976fbe8e/microsoft-fabric.png "Product Avatar Image")](https://www.g2.com/products/microsoft-fabric/reviews)[Microsoft Fabric](https://www.g2.com/products/microsoft-fabric/reviews)[4.7/5(45)](https://www.g2.com/products/microsoft-fabric/reviews) | Unified data-to-analytics pipelines inside Microsoft ecosystem | "Finally got our data stack in one place, but costs need attention" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_24bb2b0b5af8e7d875ea09d767bcb097/ibm-watsonx-ai.jpg "Product Avatar Image")](https://www.g2.com/products/ibm-watsonx-ai/reviews)[IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)[4.4/5(153)](https://www.g2.com/products/ibm-watsonx-ai/reviews) | Enterprise AI governance with foundation model deployment | "Comprehensive One-Stop Platform for Building and Testing AI Workflows" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_b3390b4cc3d92e87d570895f7358c003/amazon-sagemaker.jpg "Product Avatar Image")](https://www.g2.com/products/amazon-sagemaker/reviews)[Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews)[4.3/5(57)](https://www.g2.com/products/amazon-sagemaker/reviews) | End-to-end ML workflows inside AWS ecosystem | "Fully Managed End-to-End ML in AWS with Powerful Distributed Training" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_32d733a305a4eebad1e1ed85ca724f92/roboflow.jpg "Product Avatar Image")](https://www.g2.com/products/roboflow/reviews)[Roboflow](https://www.g2.com/products/roboflow/reviews)[4.7/5(155)](https://www.g2.com/products/roboflow/reviews) | Computer vision dataset annotation to deployment | "Speeds up our agri‑CV research" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2b00e05c107c3273cea5264090c3c1d0/snowflake.jpg "Product Avatar Image")](https://www.g2.com/products/snowflake/reviews)[Snowflake](https://www.g2.com/products/snowflake/reviews)[4.5/5(763)](https://www.g2.com/products/snowflake/reviews) | ML pipelines on centralized multi-source data | "Elastic Scaling and Fast Analytics with Snowflake" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_c3e4922bb6835a32854c1dead2cda2bb/sas-sas-viya.jpg "Product Avatar Image")](https://www.g2.com/products/sas-sas-viya/reviews)[SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)[4.3/5(817)](https://www.g2.com/products/sas-sas-viya/reviews) | Enterprise ML governance with SAS code continuity | "Effective Data Analysis with SAS Viya" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_f176b4154a751d10150daa67a57b7dc5/azure-machine-learning-studio.jpg "Product Avatar Image")](https://www.g2.com/products/microsoft-azure-machine-learning/reviews)[Azure Machine Learning Studio](https://www.g2.com/products/microsoft-azure-machine-learning/reviews)[4.3/5(90)](https://www.g2.com/products/microsoft-azure-machine-learning/reviews) | Beginner-friendly model deployment with Azure integration | "Cost-Efficient Medical Data Integration Backed by Great Support" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_791b528c516cc1b08151fa6da3988161/dataiku.png "Product Avatar Image")](https://www.g2.com/products/dataiku/reviews)[Dataiku](https://www.g2.com/products/dataiku/reviews)[4.4/5(224)](https://www.g2.com/products/dataiku/reviews) | Cross-functional ML workflows with visual and code flexibility | "Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity" |

* * *

Show More

### MLOps Platforms Topics

- [What are MLOps Platforms?](#what-are-mlops-platforms)
- [What are the Common Features of MLOps Platforms?](#what-are-the-common-features-of-mlops-platforms)
- [What are the Benefits of MLOps Platforms?](#what-are-the-benefits-of-mlops-platforms)
- [Who Uses MLOps Platforms?](#who-uses-mlops-platforms)
- [What are the Alternatives to MLOps Platforms?](#what-are-the-alternatives-to-mlops-platforms)
- [Challenges with MLOps Platforms](#challenges-with-mlops-platforms)
- [Which Companies Should Buy MLOps Platforms?](#which-companies-should-buy-mlops-platforms)
- [How to Buy MLOps Platforms](#how-to-buy-mlops-platforms)
- [What Do MLOps Platforms Cost?](#what-do-mlops-platforms-cost)
- [Implementation of MLOps Platforms](#implementation-of-mlops-platforms)
- [MLOps Platforms Trends](#mlops-platforms-trends)

[
### MLOps Platforms Topics
Expand/Collapse ](#)
- [What are MLOps Platforms?](#what-are-mlops-platforms)
- [What are the Common Features of MLOps Platforms?](#what-are-the-common-features-of-mlops-platforms)
- [What are the Benefits of MLOps Platforms?](#what-are-the-benefits-of-mlops-platforms)
- [Who Uses MLOps Platforms?](#who-uses-mlops-platforms)
- [What are the Alternatives to MLOps Platforms?](#what-are-the-alternatives-to-mlops-platforms)
- [Challenges with MLOps Platforms](#challenges-with-mlops-platforms)
- [Which Companies Should Buy MLOps Platforms?](#which-companies-should-buy-mlops-platforms)
- [How to Buy MLOps Platforms](#how-to-buy-mlops-platforms)
- [What Do MLOps Platforms Cost?](#what-do-mlops-platforms-cost)
- [Implementation of MLOps Platforms](#implementation-of-mlops-platforms)
- [MLOps Platforms Trends](#mlops-platforms-trends)

## Learn More About MLOps Platforms

### What are MLOps Platforms?

MLOps solutions apply tools and resources to ensure that machine learning projects are run properly and efficiently, including data governance, model management, and model deployment.

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 machine learning, users are enabled to mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and helps make data-driven predictions.

One crucial aspect of the machine learning process is the development, management, and monitoring of machine learning models. Users leverage MLOps Platforms to manage and monitor machine learning models as they are integrated into business applications. 

Although MLOps capabilities can come together in software products or platforms, it is fundamentally a methodology. When data scientists, data engineers, developers, and other business stakeholders collaborate and ensure that the data is properly managed and mined for meaning, they need MLOps to ensure that teams are aligned, and that machine learning projects are tracked and can be reproduced.

#### What Types of MLOps Platforms Exist?

Not all MLOps Platforms are created equal. These tools allow developers and data scientists to manage and monitor machine learning models. However, they differ in terms of the data types supported, as well as the method and manner of deployment. 

**Cloud**

With the ability to store data in remote servers and easily access them, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insights from it as well as to ensure its quality. These platforms allow them to 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 which have been deployed.

**On-premises**

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 a number of reasons, including data security and latency issues. In cases like health care, strict regulations such as HIPAA require data to be secure. Therefore, on-premises solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes vital.

**Edge**

Some platforms allow for spinning up algorithms on the edge, which consists of a mesh network of data centers that process and store data locally prior to being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** **

### What are the Common Features of MLOps Platforms?

The following are some core features within MLOps Platforms that can be useful to users:

**Model training:** Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and results in improved model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process whereby the proper values are determined for all the weights and the bias from the inputted data. Two key methods used for this purpose are supervised learning and 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 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. It can help with recording, cataloging, and organizing all machine learning models deployed across the business. Not all models are meant for all users. Therefore, some tools allow for provisioning users based on authorization to both deploy and iterate upon machine learning models.

**Model deployment:** The deployment of machine learning models is the process of making the models available in production environments, where they provide predictions to other software systems. Some tools allow users to manage model artifacts and track which models are deployed in production. Methods of deployments take the form of REST APIs, GUI for on-demand analysis, and more.

**Metrics:** Users can control model usage and performance in production. This helps track how the models are performing.

### What are the Benefits of MLOps Platforms?

Through the use of MLOps Platforms, data scientists can gain visibility into their machine learning endeavors. This helps them better understand what is and isn’t working, and they are provided 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 are enabled to share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork.

**Simplify and scale data science:** Pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms efficiently help scale experiments across many nodes to perform distributed training on large datasets.

**Experiment better:** 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. MLOps Platforms facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for deep learning are also used in experimentation, which are algorithms or methods used to change the attributes of neural networks such as weights and learning rate to reduce the losses.

### Who Uses MLOps Platforms?

Data scientists are in high demand, but there is a shortage in the number of skilled professionals available. The skillset is varied and vast (for example, there is a need to understand a vast array of algorithms, advanced mathematics, programming skills, and more); therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms are increasingly including 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 these projects. The more robust platforms provide resources that give nontechnical users the ability to understand the models, the data involved, and the aspects of the business which 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:** Especially 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 MLOps to bring AI into their organization.

**Professional data scientists:** Expert data scientists take advantage of these platforms to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment, 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 MLOps Platforms?

Alternatives to MLOps Platforms can replace this type of software, either partially or completely:

[Data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) **:** Depending on the use case, businesses might consider data science and machine learning platforms. This software provides a platform for the full end-to-end development of machine learning models and can provide more robust features around operationalizing these algorithms.

[Machine learning software](https://www.g2.com/categories/machine-learning) **:** MLOps Platforms are great for the full-scale monitoring and managing of models, whether that be for 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.

Many different types of machine learning algorithms perform various tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations looking for point solutions.

#### Software Related to MLOps Platforms

Related solutions that can be used together with MLOps 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 MLOps 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 a large number of 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, allowing business intelligence and analytics tools to pull all company data from a single repository. 

[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 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 and natural language generation (NLG), which converts data into understandable human language. Some examples of NLP uses include chatbots, translation applications, and social media monitoring tools that scan social media networks for mentions.

### Challenges with MLOps Platforms

Software solutions can come with their own set of challenges. 

**Data requirements:** For most AI algorithms, a great deal of data is required to make it learn the needful. 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 actions they need. 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 with 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 MLOps 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:** The use of AI in financial services is prolific, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With MLOps Plat, data science teams can build models with company data and deploy them to both 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 Buy MLOps Platforms

#### Requirements Gathering (RFI/RFP) for MLOps Platforms

If a company is just starting out and looking to purchase their 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, they must 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 scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data science platform.

#### Compare MLOps Platforms

**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 the comparison is thoroughgoing, the user should demo each solution on the short list with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.

#### Selection of MLOps Platforms

**Choose a selection team**

Before getting started, creating a winning team that will work together throughout the entire process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interest, 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 for recommending 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.

### What Do MLOps Platforms Cost?

As mentioned above, MLOps Platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs related to setting up the infrastructure. 

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 often not have as many features and may have caps on usage. 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 MLOps Platforms to derive some degree of ROI. As they are looking to recoup the losses from the software, it is critical to understand its costs. As mentioned above, these platforms are typically billed per user, 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 MLOps Platforms

**How are MLOps Platforms 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 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 MLOps Platforms Implementation?**

It may require a lot of people, or many 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, it is rare that one person or even one team has a complete understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together their data and begin the journey of data science, starting with proper data preparation and management.

**What Does the Implementation Process Look Like for MLOps Platforms?**

In terms of implementation, it is typical for the platform deployment to begin in a limited fashion and subsequently roll out in a broader fashion. For example, a retail brand might decide to A/B test their use of a personalization algorithm for a limited number of visitors to their site to better understand 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 was not successful, the team could go back to the drawing board, attempting to figure out what went wrong. This will involve examining the training data, as well as the 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 as a whole.

**When Should You Implement MLOps Platforms?**

As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must prioritize getting their data in order, 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. 

### MLOps 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 are getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it or not. Using embedded AI inside software like CRM, marketing automation, and analytics solutions allows users 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 way cloud deployment and mobile capabilities have over the past decade or so. 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 offer MLaaS to other businesses.

Developers easily take advantage of these prebuilt algorithms and solutions by feeding them their own 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 businesses continue to rely on these microservices and as the need for AI increases.

**Explainability**

When it comes to machine learning algorithms, especially deep learning, it may be particularly 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. MLOps Platforms are increasingly including tools for explainability, helping users build explainability into their models and meet data explainability requirements in legislation such as the European Union's privacy law, the GDPR.