Overview
The freedom data science teams need delivered by a cloud-native service that works for IT.
Cloudera Machine Learning (CML) enables enterprise data science teams to collaborate across the full data lifecycle with immediate access to enterprise data pipelines, scalable compute resources, and access to preferred tools. Streamline the process of getting analytic workloads into production and intelligently manage machine learning use cases across the business at scale.
CML on CDP optimizes ML workflows across your business with native and robust tools for deploying, serving, and monitoring models. With extended SDX for models, govern and automate model cataloging and then seamlessly move results to collaborate across CDP experiences including Cloudera Data Warehouse and Operational Database.
For data scientists
Optimize the ML data lifecycle and operationalize machine learning models across the business with transparent and repeatable workflows that work for everyone — from IT to decision makers.
For IT leaders
Give your data science teams the resources they need to experiment, iterate, and impact the business anywhere, without compromising agility, security, or governance.
Use cases
Cost-effectively operate models
Burst model training to the cloud
Quickly experiment, iterate, and tune
Cost-effectively operate models
The complete toolset for production machine learning at scale.
Deploy models into production with a few clicks and manage all of your production environment from a single pane of glass. Leverage Cloudera Shared Data Experience (SDX) for models, monitor prediction metrics, ground truth production applications, and quickly identify model drift before it impacts your business.
Burst model training to the cloud
Burst machine learning model training to the cloud.
On-premises infrastructure too busy to take on machine learning processing for model training? Easily and securely replicate entire projects from on-premises-to-cloud, enabling compute-heavy processes to run without impacting other teams.
Quickly experiment, iterate, and tune
Experiment and tune your machine learning models securely.
Experimentation is key when developing and deploying machine learning models across your enterprise. With experiments in Cloudera Machine Learning, enable your data science teams to test various approaches and perform hyperparameter tuning to best serve your business.
Deploy machine learning workspaces in a few clicks, giving data science teams access to the project environments and automatically elastic compute resources they need for end-to-end ML without waiting.
With Cloudera Machine Learning, administrators and data science teams have full visibility from data source to production environment — enabling transparent workflows and easy collaboration across teams securely.
Cloudera Machine Learning offers both a robust built in workbench and the flexibility to natively use their favorite tools such as Jupyter Notebooks and RStudio while preserving security, efficiency and scalability without administrative overhead.
Always achieve the optimal outcome with advanced experimentation capabilities for hyperparameter tuning and multi-model testing for production workloads.
Cloudera Machine Learning’s MLOps capability enables one click model deployment, model cataloging and granular prediction monitoring to keep models secure and accurate across production environments
Deploy CML anywhere with a cloud native, portable, and consistent experience from your data center to any public cloud. Run hybrid and multi-cloud architectures without creating disconnected silos or requiring new workflows.
Deployment options
Deploy Cloudera Machine Learning anywhere with a cloud-native, portable, and consistent experience everywhere.
CDP Public Cloud
- Multi-cloud ready: Don’t get locked into a single cloud provider. Power your AI initiatives holistically with data from anywhere
- Scalable: Leverage scalable and auto-suspending compute resources that you only pay for while in use
- Full lifecycle integration: Seamlessly and securely share workloads and outputs across CDP experiences including Cloudera Data Engineering and Data Warehouse
CDP Private Cloud
- Cost-effective: Optimized resource utilization from disaggregated storage affords cost-efficiencies across the cluster
- Optimized performance: Meet your SLAs every time with workload isolation and multi-tenancy for critical workloads
- Collaborate effectively: Securely share workloads, data, and results across teams at every stage of the data lifecycle
Getting started
CDP demo
Watch a technical demo to learn how to connect the lifecycle of your data to power AI use cases.
Discover CDP video tour
Look under the hood with a video tour of CDP and discover how secure and optimized ML workflows can better serve your business.
CDP technical resources
Save time with technical information and resources to help you develop your skills and gain knowledge about Cloudera Machine Learning.
Free training
Access on-demand training to get up to speed with CML on CDP to enable streamlined, self-service machine learning across the enterprise.
Pricing
Evaluate pricing, billing terms, licensing details, and hourly rates as well as estimate costs with handy calculators.
Product documentation
Get started on the right foot with resource planning, product configuration, and everything you need for ML best practices.
