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The Forrester WaveTM: Notebook-Based Predictive Analytics and Machine Learning, Q3 2020


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.

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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 ML at scale.

Deploy models into production with a few clicks and manage all of your production environment from a single pane of glass. Leverage CDP Shared Data Experience (SDX) for models, monitor prediction metrics, ground truth production applications, and quickly identify model drift before it impacts your business.

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.

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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.

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

600PB of mobile data volume managed.

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Quickly experiment, iterate, and tune

Experiment and tune your ML models securely.

Experimentation is key when developing and deploying machine learning models across your enterprise. With experiments in CDP Machine Learning, enable your data science teams to test various approaches and perform hyperparameter tuning to best serve your business.

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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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 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 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 CDP Machine Learning 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 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.

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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.

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CDP technical resources

Save time with technical information and resources to help you develop your skills and gain knowledge about Cloudera Machine Learning.

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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.

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Evaluate pricing, billing terms, licensing details, and hourly rates as well as estimate costs with handy calculators.

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Product documentation

Get started on the right foot with resource planning, product configuration, and everything you need for ML best practices.

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Analyst Report

The Forrester WaveTM: Notebook-Based Predictive Analytics and Machine Learning, Q3 2020


Enabling production MLOps at scale


4 essential platform factors for enterprise ML


10 steps to making machine learning operational

World-class training, support, & services

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