Overview
The freedom data science teams need delivered by a cloud-native service that works for IT.
CDP Machine Learning 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.
CDP Machine Learning 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 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.
Experience the tools for yourself
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
TAKE AI FROM CONCEPT TO REALITY
SCALE MACHINE LEARNING WITH MLOPS
ENABLE EXPLORATORY DATA SCIENCE
Take AI from concept to reality
Reduce your time to value and get AI applications off the ground.
Whatever industry you’re in, business challenges are arriving faster than ever. Increasingly it takes AI and ML tools to keep pace. You may feel pressure to deliver a strategy that puts AI applications into practice. CML with CDP is the unified data platform that will help you move AI from the lab to the factory.
Scale machine learning with MLOPS
Benefit from improved transparency, collaboration, and ROI with MLOps.
MLOps helps you capitalize on early successes and scale up, following steps to keep existing models current, and establishing controls to maintain data security and governance through the entire production ML lifecycle.
Enable exploratory data science
Compress the space between data exploration and business action.
Cloudera offers a complete platform that provides data science teams with “certified datasets,” as well as consistent and robust tooling to make data explorations, ad-hoc data science, and insight generation as fast as possible.
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 CDP 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 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.
Deliver insights with a consistent and easy-to-use experience, featuring intuitive and accessible drag-and-drop dashboards and custom application creation.
Ready to take a deeper look?
Experience Machine Learning on Cloudera Data Platform for yourself
Deployment options
Deploy CDP 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
For the Private Cloud Base form factor of CDP Machine Learning, please see Cloudera Data Science Workbench.
Get started
CDP weekly demo
Join Cloudera technical experts for a weekly demo and live Q&A. 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.
What customers say about Cloudera Machine Learning
“Integrates seamlessly with the other CDP experiences, enabling rapid realization of insights from your data. I especially appreciate the flexibility and openness.”
Analytics Solution Architect
Energy and Utilities Industry
“One-stop-shop for your data science needs. Managing multiple sessions, automating data pipeline jobs, and even creating machine learning apps are all easy and intuitive.”
Model Development Expert
Services Industry
“Excellent platform for any type of ML and Data engineering project. Provides an easy path to develop and test code as well as ML performance tracking.”
Big Data And Analytic Architect
Miscellaneous Industry