Cloudera Data Science Workbench overview

Machine learning has become one of the most critical capabilities for modern businesses to grow and stay competitive today. From automating internal processes to optimizing the design, creation, and marketing processes behind virtually every product consumed, ML models have permeated almost every aspect of our work and personal lives.

ML development is iterative and complex, made even harder because most ML tools aren’t built for the entire machine learning lifecycle. Cloudera Data Science Workbench on Cloudera Data Platform accelerates time-to-value by enabling data scientists to collaborate in a single unified platform that is all inclusive for powering any AI use case. Purpose-built for agile experimentation and production ML workflows, Cloudera Data Science Workbench manages everything from data preparation to MLOps, to predictive reporting. Solve mission critical ML challenges along the entire lifecycle with greater speed and agility to discover opportunities which can mean the difference for your business.

Each ML workspace enables teams of data scientists to develop, test, train, and ultimately deploy machine learning models for building predictive applications all on the data under management within the enterprise data cloud. ML workspaces support fully-containerized execution of Python, R, Scala, and Spark workloads through flexible and extensible engines.

Core Capabilities

Cloudera Data Science Workbench covers the end-to-end machine learning workflow, enabling fully isolated and containerized workloads - including Python, R, and Spark-on-Kubernetes - for scale-out data engineering and machine learning with seamless distributed dependency management.

  • Sessions enable Data Scientists to directly leverage the CPU, memory, and GPU compute available across the workspace, while also being directly connected to the data in the data lake.

  • Experiments enable Data Scientists to run multiple variations of model training workloads, tracking the results of each Experiment in order to train the best possible Model.

  • Models can be deployed in a matter of clicks, removing any roadblocks to production. They are served as REST endpoints in a high availability manner, with automated lineage building and metric tracking for MLOps purposes.

  • Jobs can be used to orchestrate an entire end-to-end automated pipeline, including monitoring for model drift and automatically kicking off model re-training and re-deployment as needed.

  • Applications deliver interactive experiences for business users in a matter of clicks. Frameworks such as Flask and Shiny can be used in development of these Applications, while Cloudera Data Visualization is also available as a point-and-click interface for building these experiences.


Cloudera Data Science Workbench is built for the agility and power of cloud computing, but is not limited to any one provider or data source. It is a comprehensive platform to collaboratively build and deploy machine learning capabilities at scale.

Cloudera Data Science Workbench provides benefits for each type of user.

Data Scientists

    • Enable DS teams to collaborate and speed model development and delivery with transparent, secure, and governed workflows

    • Expand AI use cases with automated ML pipelines and an integrated and complete production ML toolkit

    • Empower faster decision making and trust with end-to-end visibility and auditability of data, processes, models, and dashboards


  • Increase DS productivity with visibility, security, and governance of the complete ML lifecycle

  • Eliminate silos, blindspots, and the need to move/duplicate data with a fully integrated platform across the data lifecycle.

  • Accelerate AI with self-service access and containerized ML workspaces that remove the heavy lifting and get models to production faster

Business Users

  • Access interactive Applications built and deployed by DS teams.

  • Be empowered with predictive insights to more intelligently make business decisions.