Overview
The freedom data science teams need on a platform that works for IT
Enterprise data science teams need access to business data and the tools and computing resources required for end-to-end machine learning workflows, while IT and the business need to maintain data governance and control infrastructure costs. Cloudera Machine Learning brings the agility and economics of cloud to self-service machine learning workflows with governed business data and tools that data science teams need, anywhere.
Use cases
Burst machine learning model training to the cloud
Burst large-scale batch scoring to the cloud
End-to-end ML with data born in the cloud
Burst machine learning model training to the cloud
On-premises infrastructure too busy to take on machine learning processing for model training? Cloudera Machine Learning lets IT easily replicate governed business data from on-premises-to-cloud and deploy new ML workspaces for teams with pre-configured resource consumption guardrails that deliver access to the data, tools, and computing resources needed for model training and deployment using data born on-premises.
Burst large-scale batch scoring to the cloud
When ML models are trained on-premises but inference data lives in the cloud, Cloudera Machine Learning enables batch scoring on large amounts of data such as image or sensor data that resides in cloud storage. IT can deploy ML workspaces with pre-configured resource utilization guardrails so data science teams can quickly process data using auto-scaling and auto-suspending TensorFlow or Spark jobs without runaway costs.
End-to-end ML with data born in the cloud
Sometimes data for machine learning projects are born in the cloud. Cloudera Machine Learning lets IT deploy new ML workspaces for teams with pre-configured resource consumption guardrails that deliver access to the tools and computing resources needed for model training and deployment using data born in the cloud.
Cloudera Machine Learning lets administrators deploy new machine learning workspaces for teams in a few clicks, giving data science teams access to the project environments and resources they need for end-to-end ML without waiting.
With Cloudera Machine Learning, administrators can easily replicate governed data sets across hybrid and multi-cloud environments to give data science teams self-service access to the business data they need while maintaining enterprise data security and governance controls.
Beyond Python, R and Scala for Spark, modern data science teams need the latest open source tools and libraries for innovation and to collaborate while working in their preferred IDE. Cloudera Machine Learning gives practitioners the freedom to use their favorite tools while preserving security, efficiency and scalability without administrative overhead.
Innovation can be unpredictable but should be unstoppable. Cloudera Machine Learning gives data science teams access to the scale-out, heterogeneous computing resources they need to get work done fast while maintaining adjustable guardrails that help IT easily manage and optimize infrastructure resources and costs.
Machine learning can’t begin until data is ready, and it doesn’t end when a model is trained. ML for business requires data engineering, model training and experiment tracking, and deploying and managing models in production. Cloudera Machine Learning gives teams the tools for it all in one cohesive environment without switching or stitching.
In a hybrid or even multi-cloud world, shouldn’t your ML platform be portable? Cloudera Machine Learning lets the business move data and infrastructure anywhere without creating disconnected silos and without changing the consistent user-experience that data science teams rely on for building robust workflows and processes for end-to-end ML.
Enterprise-grade security and governance
Secure and govern platform data and metadata, and control capabilities with dedicated, integrated interfaces to manage it. Data security, governance and control policies are set once and consistently enforced everywhere, reducing operational costs and business risks while also enabling complete infrastructure choice and flexibility.
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