Best AI/ML Model Training Platforms for Databricks Data Intelligence Platform
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Compare the Top AI/ML Model Training Platforms that integrate with Databricks Data Intelligence Platform as of July 2025
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This a list of AI/ML Model Training platforms that integrate with Databricks Data Intelligence Platform. Use the filters on the left to add additional filters for products that have integrations with Databricks Data Intelligence Platform. View the products that work with Databricks Data Intelligence Platform in the table below.
What are AI/ML Model Training Platforms for Databricks Data Intelligence Platform?
AI/ML model training platforms are software solutions designed to streamline the development, training, and deployment of machine learning and artificial intelligence models. These platforms provide tools and infrastructure for data preprocessing, model selection, hyperparameter tuning, and training in a variety of domains, such as natural language processing, computer vision, and predictive analytics. They often include features for distributed computing, enabling the use of multiple processors or cloud resources to speed up the training process. Additionally, model training platforms typically offer integrated monitoring and debugging tools to track model performance and adjust training strategies in real time. By simplifying the complex process of building AI models, these platforms enable faster development cycles and more accurate predictive models. Compare and read user reviews of the best AI/ML Model Training platforms for Databricks Data Intelligence Platform currently available using the table below. This list is updated regularly.
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1
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
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Flyte
Union.ai
The workflow automation platform for complex, mission-critical data and ML processes at scale. Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing. Flyte is used in production at Lyft, Spotify, Freenome, and others. At Lyft, Flyte has been serving production model training and data processing for over four years, becoming the de-facto platform for teams like pricing, locations, ETA, mapping, autonomous, and more. In fact, Flyte manages over 10,000 unique workflows at Lyft, totaling over 1,000,000 executions every month, 20 million tasks, and 40 million containers. Flyte has been battle-tested at Lyft, Spotify, Freenome, and others. It is entirely open-source with an Apache 2.0 license under the Linux Foundation with a cross-industry overseeing committee. Configuring machine learning and data workflows can get complex and error-prone with YAML.Starting Price: Free -
3
OPAQUE
OPAQUE Systems
OPAQUE Systems offers a leading confidential AI platform that enables organizations to securely run AI, machine learning, and analytics workflows on sensitive data without compromising privacy or compliance. Their technology allows enterprises to unleash AI innovation risk-free by leveraging confidential computing and cryptographic verification, ensuring data sovereignty and regulatory adherence. OPAQUE integrates seamlessly into existing AI stacks via APIs, notebooks, and no-code solutions, eliminating the need for costly infrastructure changes. The platform provides verifiable audit trails and attestation for complete transparency and governance. Customers like Ant Financial have benefited by using previously inaccessible data to improve credit risk models. With OPAQUE, companies accelerate AI adoption while maintaining uncompromising security and control. -
4
Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built.
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