DataRobot - Detailed Review
Data Tools
DataRobot - Product Overview
Introduction to DataRobot
DataRobot is an automated machine learning platform that simplifies and accelerates the process of building, deploying, and managing predictive models. Here’s a breakdown of its primary function, target audience, and key features:Primary Function
DataRobot automates the entire machine learning workflow, from data ingestion and preparation to model selection, training, and deployment. This automation enables users to focus on interpreting results and making data-driven decisions, rather than spending time on the intricate details of model building and tuning.Target Audience
DataRobot’s primary users include data scientists, business analysts, and organizations across various industries such as finance, healthcare, retail, manufacturing, and more. The platform is particularly beneficial for large enterprises and mid-sized companies looking to leverage AI and machine learning to drive innovation and optimize decision-making. It also caters to data professionals seeking advanced tools for data modeling and predictive analytics.Key Features
Data Preparation and Automation
DataRobot automates data cleaning and preprocessing, handling tasks such as missing values and categorical variables. It also performs automated feature engineering, creating new features that can enhance model performance.Model Selection and Training
The platform automatically selects and trains multiple machine learning models, evaluating hundreds of models in parallel using techniques like cross-validation. It ranks these models based on performance metrics such as accuracy, precision, and recall, ensuring the best-performing model is selected.User-Friendly Interface
DataRobot features a drag-and-drop interface that is intuitive and easy to use, even for those without deep knowledge of AI/ML or coding. This interface includes version control and flexible data visualization, making it easy to build, deploy, and customize models.Production and Deployment
The platform offers various deployment options and integrations with external systems, allowing for the seamless implementation of models. It also includes features for validation, resource management, and ROI monitoring, ensuring models are tested, approved, and optimized in real-time.Additional Products
DataRobot provides additional tools such as a no-code app builder, automated time series forecasting, and DataRobot University, which offers training programs and certifications in AI and ML.Benefits
DataRobot’s automation significantly reduces the time and resources required for predictive modeling, providing an efficient feedback loop between data preparation and model building. The platform’s collaborative features allow teams to securely store and work on projects in real-time, enhancing collaboration and project management. DataRobot - User Interface and Experience
User-Friendly Interface
DataRobot’s interface is designed to be clear and easy to use. It features a clean look and feel, with better organization of key assets and intuitive navigation. This redesign, as seen in the latest version, DataRobot 9.0, enhances rapid experimentation and makes the workflow more efficient.
Data Ingestion and Preparation
Users can easily upload their data from various sources such as local machines, cloud storage, or databases like SQL. The platform supports multiple file formats, including CSV, Excel, and JSON. Once the data is uploaded, DataRobot automatically performs data cleaning and preprocessing, handling tasks like missing values and categorical variables, which saves significant time and effort.
Automated Machine Learning
The platform automates the entire machine learning process, from data ingestion to model deployment. Users can select their target variable, and DataRobot will automatically select, train, and evaluate hundreds of models in parallel, ranking them based on performance metrics. This automation allows users to focus on interpreting results and making data-driven decisions rather than spending time on manual model selection and tuning.
Visualizations and Feedback
DataRobot provides clear visualizations and easy-to-understand explanations of model performance and feature importance. Users can explore their data, monitor the progress of model training, and evaluate results without needing deep technical expertise. The platform offers a graphical user interface (GUI) that is intuitive and easy to read, making it accessible to both technical and non-technical users.
Collaboration and Project Management
The platform supports collaboration by allowing teams to store project information in shared folders hosted in the cloud. All authorized participants can view, comment, and work on projects, and the platform keeps a constant record of project tasks and activities in real time. This facilitates teamwork and ensures that all stakeholders are informed and involved.
Deployment and Monitoring
Deploying models with DataRobot is straightforward and efficient. Users can deploy models to production with just a few clicks, integrating them into existing systems via REST APIs. The platform supports both batch and real-time predictions and provides monitoring tools to track the performance of deployed models, ensuring continuous reliability.
Training and Documentation
DataRobot offers a comprehensive set of training materials, including video tutorials, explanations of key concepts, and workflow overviews. The UI documentation covers all aspects of working with DataRobot, from data import to deployment and model management. Additionally, API documentation provides detailed instructions for creating and managing models via REST or using DataRobot Python/R clients.
Overall User Experience
The overall user experience with DataRobot is positive, with users praising its ease of use, efficiency, and the significant reduction in time and resources required for predictive modeling. Customers have highlighted that DataRobot helps maximize productivity and revenue goals across AI projects, and the platform’s automation capabilities and user-friendly interface make it a valuable tool for both small teams and large enterprises.
DataRobot - Key Features and Functionality
DataRobot Overview
DataRobot is an automated machine learning platform that simplifies and accelerates the process of building, deploying, and managing predictive and generative AI models. Here are the key features and how they work:Data Ingestion and Preparation
DataRobot allows you to upload your dataset from various sources, including local machines, cloud storage, and databases like SQL, Amazon S3, and Snowflake. It supports multiple file formats such as CSV, Excel, and JSON. Once uploaded, DataRobot automatically performs data cleaning and preprocessing, handling tasks like missing values and categorical variables. This automation ensures your data is ready for model training without extensive manual intervention.Automated Feature Engineering
The platform uses automated feature engineering to create new features from your dataset, which can enhance the predictive power of your models. DataRobot examines your data, identifies patterns, and generates additional features such as interaction terms, aggregated data, and transformed variables. This process helps ensure that your models have access to the most relevant and powerful predictors.Model Selection and Training
DataRobot automates the selection and training of multiple machine learning models. It supports a wide range of algorithms, including decision trees, gradient boosting machines, and neural networks. The platform evaluates hundreds of models in parallel using techniques like cross-validation to assess their performance. Models are ranked based on metrics such as accuracy, precision, recall, and F1 score, ensuring you get the best-performing model without manual testing.Model Evaluation and Interpretation
After training, DataRobot provides a detailed evaluation of the models, including accuracy, feature importance maps, receiver operating characteristic (ROC) curves, and prediction explanations. This allows users to interpret results and make data-driven decisions efficiently. The platform also includes an integrated leaderboard that ranks models based on their performance metrics.User-Friendly Interface
DataRobot features a user-friendly interface that makes it accessible to both data scientists and business analysts. You can easily upload data, select the target variable, and let DataRobot handle the rest. The platform supports low-code and no-code (LCNC) design, making advanced analytics accessible to a wide range of users.Integrations and Deployment
DataRobot offers seamless integrations with various data platforms, including data warehouses, data lakes, on-prem databases, and cloud storage services like Amazon S3 and Snowflake. You can deploy models within DataRobot or to other platforms such as Amazon SageMaker or Snowflake. The platform also supports custom applications, business applications, and integrations with large language models (LLMs) and other AI infrastructure.Collaboration and Transparency
DataRobot facilitates collaboration among team members by allowing them to share and contribute to machine learning project folders. This ensures transparency and a permanent record of project tasks and activities, making it easier to train new team members and start new projects.Generative AI Capabilities
In addition to predictive AI, DataRobot also offers generative AI capabilities, enabling users to build and deliver end-to-end generative AI solutions. This includes features and demos specifically designed for generative AI use cases, enhancing innovation and impact.Conclusion
Overall, DataRobot streamlines the machine learning process, reducing the time to deployment and enabling users to focus on interpreting results and making data-driven decisions. Its automation, user-friendly interface, and extensive integrations make it a valuable tool for enterprises looking to leverage AI effectively. DataRobot - Performance and Accuracy
Evaluating the Performance and Accuracy of DataRobot
Evaluating the performance and accuracy of DataRobot, a prominent AI-driven data tool, involves several key aspects and some notable limitations.
Performance Evaluation
DataRobot is renowned for its ability to automate and streamline the process of building, deploying, and monitoring predictive models. Here are some highlights:
- Automated Data Preparation: DataRobot simplifies data preparation by automatically handling tasks such as data cleaning, feature engineering, and transformation. This is particularly useful as it minimizes the need for manual intervention, making the process more efficient.
- Model Building and Deployment: The platform supports a wide range of model types, including time series, regression, and classification models. It also provides tools for backtesting and validating models before deployment, ensuring that the models perform well over time.
- Real-time Monitoring: DataRobot offers real-time monitoring capabilities through its Accuracy tab, which allows users to track the performance of deployed models over time. This includes visualizations and alerts for any decline in model accuracy, helping users identify when a model might need retraining or replacement.
Accuracy Metrics and Analysis
Accuracy is a critical metric in DataRobot, and the platform provides several tools to analyze and maintain model accuracy:
- Accuracy Tab: This feature allows users to analyze model performance using standard statistical measures and exportable visualizations. It helps in determining if a model’s quality is decaying and if it needs to be replaced. The tab provides color-coded alerts (green, yellow, red, and gray) to indicate the model’s performance status.
- Accuracy Over Time: This tool visualizes predicted and actual values over time, helping users to identify any unexplained trends in the data that the model did not account for. It is particularly useful for time series experiments where forecast distances can be adjusted.
Limitations and Areas for Improvement
While DataRobot offers significant capabilities, there are some limitations and areas that require attention:
- Data Processing Limits: The accuracy scores displayed may not reflect every row of larger prediction requests due to data processing limits. Users need to span requests over multiple hours or days to avoid reaching computation limits and achieve more precise scores.
- Minimum Prediction Requirements: To enable the Accuracy tab, a minimum of 100 rows of predictions with corresponding actual values is required. If this threshold is not met, accuracy tracking will not be available.
- Time Zone and Date Considerations: DataRobot does not support multiple time zones or daylight savings time. Users need to convert their data to a single time zone outside of the platform. Additionally, dates before 1900 are not supported and need to be shifted forward in time.
- Model-Specific Constraints: Certain models, such as DeepAR and temporal hierarchical models, have specific requirements and limitations. For example, DeepAR supports only regression projects and requires certain features to be available at prediction time.
Data Handling and Integration
DataRobot has enhanced its data handling capabilities, but there are still some constraints:
- Dataset Size Limits: The sum of all dataset sizes should not exceed 40GB, and individual dataset sizes should not exceed 20GB. Larger datasets may not be fully supported.
- Data Connectivity and Preparation: While DataRobot offers seamless data integration and preparation tools, including secure data connectivity and the ability to wrangle, join, and aggregate data, these features are optimized for specific use cases and may require updates for on-prem solutions.
In summary, DataRobot is a powerful tool for building and deploying predictive models, with strong capabilities in automated data preparation, real-time monitoring, and accuracy analysis. However, users need to be aware of the data processing limits, minimum prediction requirements, and specific model constraints to fully leverage the platform’s potential.
DataRobot - Pricing and Plans
DataRobot Pricing Structure
DataRobot’s pricing structure is designed to be flexible and scalable, catering to various business needs and user requirements. Here are the key points regarding their pricing and plans:
Subscription-Based Model
DataRobot operates on a subscription-based model, where customers pay a recurring fee to access and use the platform. This model allows for flexibility and scalability, enabling customers to adjust their usage based on their needs.
Pricing Tiers
DataRobot employs a tiered pricing structure, offering different plans to accommodate different levels of usage, user access, and advanced features. Here are some general insights into these tiers:
- Basic Plans: These plans are suitable for small businesses and may include limited features and user access. The cost can start at around $2,500 per month for a basic package.
- Advanced Plans: These plans are designed for larger enterprises and include more advanced analytics, machine learning capabilities, and predictive modeling. The costs can range from $15,000 to $20,000 per month for 10 users, and up to $500,000 annually for global enterprise levels with 1,000 users.
Enterprise Plan
The Enterprise plan is not based on credits and is billed more directly. This plan is typically customized to meet the specific needs of large organizations and includes comprehensive support and advanced features.
Credit-Based Free Trial
DataRobot offers a free trial that operates on a credit system. During the trial, you need credits to run the software, and once the credits are depleted, the account becomes read-only. The free trial has several limitations, including no exports, no support (though community support is available), limited users, and no sharing.
Discounts and Special Plans
DataRobot provides discounts for academic institutions, government agencies, and nonprofits. These discounts are intended to make AI more accessible to these sectors. To receive a discount, you need to fill out an application, and the discount amount is determined after the application is reviewed and accepted.
Professional Services
In addition to the platform subscription, DataRobot offers professional services such as consulting, training, and implementation support. These services are provided by a team of data scientists and ML experts to help customers maximize the value and impact of their AI projects.
Contract and Usage Costs
For specific deployments, such as the DataRobot AI Cloud Platform on AWS, pricing is based on contract duration, with options to pay upfront or in installments. Additional usage costs are charged based on the terms defined in the private offer contract.
In summary, DataRobot’s pricing is structured to accommodate a wide range of users and organizations, from small businesses to large enterprises, with various tiers and options for customization and support.
DataRobot - Integration and Compatibility
Integration and Compatibility of DataRobot
DataRobot, a leading AI-driven platform, is renowned for its extensive integration capabilities and broad compatibility across various tools, platforms, and devices. Here are some key aspects of its integration and compatibility:Business Applications and Systems
DataRobot allows seamless integration with the systems your business users rely on. This includes developing enterprise-grade custom applications for predictive and generative AI use cases, ensuring that AI can be embedded into existing business workflows efficiently.Data Platforms
DataRobot integrates effortlessly with a wide range of data platforms, including data warehouses, data lakes, on-premises databases, and orchestration systems. For example, the platform has native integrations with Snowflake, which enables joint users to execute data science projects in DataRobot while performing computations in Snowflake. This integration optimizes workload performance by pushing down relational operations to the Snowflake platform, reducing project runtimes.Cloud Environments
The DataRobot AI Platform is highly flexible and can be deployed on-premises or in any cloud infrastructure. It works seamlessly with all cloud environments, including Microsoft Azure. The integration with Azure provides flexible procurement options via the Azure Marketplace, easy model deployment in the Azure ecosystem, and built-in data connectors for Azure Synapse Analytics, Azure Data Lake Storage Gen 2 (ADLS), and Azure SQL Database. Models trained in DataRobot can also be easily deployed to Azure Machine Learning.AI Infrastructure and Tools
DataRobot offers broad interoperability with various AI tools and infrastructure. It supports the integration of generative tools, including vector databases and large language models (LLMs) from any provider. The platform also connects with best-in-breed components through APIs and frameworks, allowing users to build predictive and generative AI solutions.Open and Extensible Platform
The DataRobot AI Platform is intentionally open and extensible, allowing users to integrate it with a wide array of different products. This openness is crucial, especially in the quickly evolving tech ecosystem, as it enables users to bring in new tools as they emerge. Everything within the platform can be initiated and manipulated in code, providing flexibility for both graphical user interface and code-based interactions.Scalability and Security
DataRobot ensures high performance and security across different environments, whether it’s a data center, cloud, or hybrid setup. This scalability is essential for enterprise-grade AI applications, enabling businesses to grow securely through reliable AI-driven decisions.Conclusion
In summary, DataRobot’s integration and compatibility are key strengths, allowing it to seamlessly work with various business applications, data platforms, cloud environments, and AI tools, making it a versatile and powerful tool for AI-driven decision-making. DataRobot - Customer Support and Resources
Customer Support
DataRobot provides dedicated support for its users, particularly for enterprise customers. Here are some key support features:
Dedicated Support and Account Management
Enterprise users have access to dedicated support and account management, ensuring successful implementation and ongoing optimization of their machine learning projects.
Community Forums
DataRobot has an active user community where users can ask questions, share insights, and learn from other users. This community support is invaluable for resolving issues and gaining tips from experienced users.
Contact Form
Users can fill out a contact form to connect with the Sales team or other relevant departments for any inquiries or support needs.
Documentation and Resources
DataRobot offers extensive documentation and resources to help users get the most out of their platform:
Detailed Documentation
Users can access detailed documentation, tutorials, and best practices to guide them through the platform. This includes API documentation, user guides, and admin guides.
API Resources
DataRobot supports REST, Python, and R APIs, providing a programmatic alternative to the UI. The API documentation includes quickstart guides, user guides, and examples of common data science workflows.
Text AI Resources
The platform provides specific resources for working with text data, including automated feature engineering, clustering, aggregation, and imputation in time series projects, as well as tools for text mining and sentiment analysis.
AI Accelerators
DataRobot offers AI Accelerators that codify and package data science expertise into repeatable, code-first workflows. These accelerators can be used to speed up model experimentation, development, and production.
Additional Tools and Features
Model Interpretation and Evaluation
DataRobot provides tools for model interpretation, such as feature importance and partial dependence plots, which help users understand the impact of each feature on the model’s predictions. Users can also view metrics, confusion matrices, and ROC curves to evaluate model performance.
Continuous Learning and Optimization
The platform supports continuous learning and optimization, allowing users to set up automatic retraining schedules to keep their models up-to-date with new data.
These resources and support options are designed to ensure that users can effectively leverage DataRobot’s AI-driven tools to build, deploy, and maintain their machine learning models.
DataRobot - Pros and Cons
Advantages of DataRobot
DataRobot offers several significant advantages that make it a powerful tool in the AI-driven data tools category:Automated Machine Learning
DataRobot automates the entire machine learning process, from data ingestion and preparation to model selection, training, and deployment. This automation saves users significant time and effort, allowing them to focus on interpreting results and making data-driven decisions.Feature Engineering
The platform excels in feature engineering, automatically discovering, testing, and creating hundreds of valuable new features. This process, known as Feature Discovery, improves the predictive power of ML models by generating the right features based on pre-existing relationships across data sources.Ease of Use and Integration
DataRobot provides an intuitive and visuals-based design, making it accessible to a wide range of users, including business analysts, executives, IT professionals, and data scientists. It supports various data sources and file formats, and models can be easily integrated with current enterprise applications via REST API endpoints.Scalability and Flexibility
The cloud-based platform offers scalability and flexibility, allowing users to handle projects of any size. It supports a wide range of machine learning algorithms and models, including regression, classification, and time series forecasting, making it versatile for addressing diverse business problems.Data Preparation and Visualization
DataRobot automatically performs data cleaning and preprocessing, handling tasks such as missing values and categorical variables. It also provides ETL and visualization tools, making data preparation and analysis more efficient.Decision Intelligence and No-Code App Builder
The platform includes a No Code App Builder that enables users to create AI-powered applications without any coding. This tool supports collaboration and provides pre-built templates and drag-and-drop widgets, making AI applications more accessible to business users.Disadvantages of DataRobot
Despite its many advantages, DataRobot also has some notable disadvantages:Performance Issues
Users have reported performance issues with the platform, which can impact the efficiency of model building and deployment.Limited Customization
DataRobot’s automation, while beneficial, can limit deep customization of models. Users may find it challenging to modify model architectures or implement highly specific algorithms, which can restrict flexibility for highly specialized use cases.Lack of Proprietary Algorithm Integration
The platform often lacks the ability to integrate proprietary algorithms specific to unique use cases. Users may wish to integrate existing Python or R code for better efficiency, but this can be challenging within DataRobot.Cost
DataRobot’s advanced features and capabilities come at a high cost, which may be prohibitive for small businesses or individual users. The pricing plans, particularly the Professional and Enterprise plans, can be expensive.Data Privacy and Security Concerns
While DataRobot offers enterprise security integrations and various database certifications, there are still potential data privacy and security concerns that users need to consider, especially when dealing with sensitive data. Overall, DataRobot is a powerful tool for automated machine learning and predictive analytics, but it requires careful consideration of its limitations and costs. DataRobot - Comparison with Competitors
Unique Features of DataRobot
DataRobot is known for its comprehensive enterprise AI platform, which includes two independent but fully integrable machine learning model building products. Here are some of its unique features:- Algorithm Diversity: DataRobot offers a constantly expanding set of diverse, best-in-class algorithms from sources like R, Python, H2O, and Spark.
- Automated Model Building: The platform automates the process of building, deploying, and managing machine learning models, making it accessible to a wide range of users.
- Explainability and Transparency: DataRobot provides features for model explainability, which is crucial for trusting AI-driven decisions.
- Data Exploration and Visualization: The platform includes tools for data exploration and visualization, helping users to better interpret their data.
Competitors and Alternatives
Microsoft Azure Machine Learning
Microsoft Azure Machine Learning is a strong competitor, offering a visual drag-and-drop authoring environment that requires no coding. It allows users to build, test, and deploy predictive analytics solutions quickly and integrates well with other Microsoft tools like Excel.Google Cloud Vertex AI
Google Cloud Vertex AI is another significant competitor, providing a managed service for training and running machine learning models in production. It offers training and prediction services and has been used in various enterprise applications, such as identifying clouds in satellite images and ensuring food safety.Amazon Web Services (AWS) Machine Learning
AWS offers machine learning and AI services that compete directly with DataRobot. AWS provides a range of tools and services, including SageMaker, which simplifies the process of building, training, and deploying machine learning models.SAS
SAS is a well-established player in the analytics and business intelligence space, offering advanced analytics solutions that compete with DataRobot. SAS provides a wide range of tools for data management, predictive analytics, and business intelligence.Dataiku
Dataiku develops a centralized data platform that includes solutions for data preparation, visualization, machine learning, and analytic applications. It serves various sectors, including banking, pharmaceuticals, and manufacturing. Dataiku’s platform is known for its no-code, cloud-ready interface, making it accessible to a broad range of users.Databricks
Databricks is a data and AI company that specializes in unifying and democratizing data, analytics, and artificial intelligence. It offers a data intelligence platform that integrates with generative AI to enhance business operations and decision-making.Key Differences
- Integration: Microsoft Azure Machine Learning and Google Cloud Vertex AI have strong integration capabilities with their respective cloud ecosystems, which can be a significant advantage for users already invested in these platforms.
- Ease of Use: DataRobot and Dataiku are known for their user-friendly interfaces and automated processes, making them more accessible to users without extensive coding skills.
- Industry Focus: While DataRobot is broadly applicable across various industries, some competitors like DarwinAI focus on specific sectors such as visual quality inspection for manufacturers.
DataRobot - Frequently Asked Questions
What is DataRobot’s Feature Discovery and how does it improve machine learning models?
DataRobot’s Feature Discovery is an advanced feature engineering tool that automatically discovers, tests, and creates hundreds of valuable new features to improve the accuracy of machine learning models. It uses pre-existing relationships across your data sources and complex data schemes to generate the right features. Key features include intuitive and visuals-based design, automatic relationship suggestions between data points, built-in time awareness for feature calculation, and transparent and traceable steps.
How does DataRobot’s Automated Machine Learning (AutoML) work?
DataRobot’s AutoML enables users to deliver quality machine learning models with minimal time, energy, and trust. It provides a built-in scheduler, supports languages like Python, R, Scala, and SQL, and allows for quick connection to data sources. The platform also includes features for importing and version control, as well as built-in resource and task monitoring.
What is the No Code App Builder in DataRobot, and what are its key features?
The No Code App Builder by DataRobot allows users to create AI-powered applications without any coding. It offers pre-built templates and drag-and-drop widgets, automatic return of detailed predictions and what-if scenarios, and supports collaboration with AI consumers and the business community. The tool also includes pre-configured app optimization for the target outcome.
How does DataRobot’s Decision Intelligence Flows work?
DataRobot Decision Intelligence Flows enable organizations to build rules based on complex business logic to automate and accelerate the decision-making process. The Intuitive Decision Flow editor supports creating rules that combine multiple machine learning models, which can then be operationalized via DataRobot’s API. Key features include built-in support for complex rules, fast decision-making at scale, explainable and data-supported decisions, and a documented and traceable decision-making process.
What are the pricing options and limitations of DataRobot’s free trial?
DataRobot offers a free trial based on credits, which allows you to interact with the software but has several limitations. These include no exports, no support (though community support is available), limited users, and no sharing. The free trial requires you to create an account and specify your project goals to receive a confirmation email with your credit allocation. For full features, you need to purchase credits or opt for the Hosted Enterprise Cloud Version.
Are there any discounts available for using DataRobot?
Yes, DataRobot offers discounts for academic institutions, government agencies, and nonprofits. These discounts are intended to make AI more accessible to sectors that need it. To receive a discount, you need to fill out an application, and the discount amount is determined after the application is reviewed and accepted.
How does DataRobot handle data quality issues?
DataRobot automatically detects and identifies common data quality issues upon uploading data. The Data Quality Assessment report uses yellow triangles to denote these issues, such as excess zeros or outliers. DataRobot also excludes non-informative features, removes features with target leakage, and performs supervised feature reduction to keep only valuable features.
What are the key features of DataRobot’s AI Applications?
DataRobot’s AI Applications allow organizations to access and consume the output of their predictive models. Key features include turning predictive models into AI apps in minutes, no-code app building, easy-to-share and accurate insights, what-if scenario analysis, and flexible prediction making individually or in bulk.
Can DataRobot be used with various programming languages and data sources?
Yes, DataRobot supports multiple programming languages such as Python, R, Scala, and SQL. It also allows for quick connection to various data sources, making it versatile for different user needs.
What are some alternatives to DataRobot?
DataRobot has several alternatives, including H2O.ai, TensorFlow, and Amazon Sagemaker Neo. H2O.ai is a completely open-source AI product that can run on big data infrastructure or virtually on Kubernetes or Spark clusters. TensorFlow is an open-source platform developed by Google, known for its extensive library for developing and training models. Amazon Sagemaker Neo is an open-source software available through an Apache Software License, allowing you to build and deploy machine learning models on AWS or edge devices.