Cloudera Machine Learning allows users to build, deploy, and scale ML and AI applications through a repeatable industrialized approach and turn data into decisions at any scale, anywhere.
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.
Containerized ML workspaces
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.
SDX for training data & ML models
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.
CML workbench & bring your own IDE
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.
Machine Learning Experiments
Always achieve the optimal outcome with advanced experimentation capabilities for hyperparameter tuning and multi-model testing for production workloads.
Complete MLOps toolset
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
Cloud-native hybrid data architecture
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.