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The freedom data science teams need delivered by a cloud-native service that works for IT.

Cloudera 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.

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 Cloudera experiences, including Cloudera Data Warehouse and Cloudera 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.

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.

Cloudera Machine Learning Use Cases


Take AI from concept to reality

Reduce your time to value with Accelerators for ML Projects (AMPs).

AMPs are designed to jumpstart your AI initiatives by providing tailored solutions for specific use cases within Cloudera Machine Learning. They enable users to quickly achieve success by offering high-quality, pre-built reference examples that can be easily adapted to your unique requirements, reducing time to value for your projects.

CML Amps use-case

United Overseas Bank: Personalizing recommendations to millions and improving anti-money laundering detection

1M+ personalized ML recommendations saving relationship managers 1,000+ hours in manual analysis.

Read the success story

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.

Expand Production Machine Learning with MLOPS eBook

Globe Telecom: Enabling the digital lifestyle of mobile customers with a modern analytic environment

600PB of mobile data volume managed.

Read the case study

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.

data visualization product screenshot

IQVIA: Increasing prediction accuracy by four times to accelerate the pace of discovery

1 million sub-second queries performed on a 2PB data set.

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Cloudera Machine Learning key features

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. 

Data scientists shouldn't have to switch between tools to discover, query, and visualize data sets. Cloudera Machine Learning offers all of these capabilities via the Data Discovery and Visualization feature, a single UI for all your exploratory data science needs.

AMPs are Cloudera Machine Learning projects that can be deployed with one click directly from Cloudera Machine Learning. AMPs enable data scientists to go from an idea to a fully working ML use case in a fraction of the time. It provides an end-to-end framework for building, deploying, and monitoring business-ready AI applications instantly.

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 Cloudera Machine Learning for yourself

Cloudera Machine Learning Deployment Options

Deploy Cloudera Machine Learning anywhere with a cloud-native, portable, and consistent experience everywhere.

Cloudera on 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 Cloudera experiences including Data Engineering and Data Warehouse.

Cloudera on 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 Cloudera Base on private cloud form factor of Machine Learning, please see Cloudera Data Science Workbench.

What customers say about Cloudera Machine Learning

Gartner PeerInsights logo
Gartner five stars

“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

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Gartner five stars

“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

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Gartner five stars

“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

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Enabling production MLOps at scale


Open Source Models From Hugging Face


Analytics & machine learning in the cloud for dummies


The definitive guide to the machine learning lifecycle

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