Cloudera Data Science Workbench Overview

Cloudera Data Science Workbench is a secure, self-service enterprise data science platform that lets data scientists manage their own analytics pipelines, thus accelerating machine learning projects from exploration to production. It allows data scientists to bring their existing skills and tools, such as R, Python, and Scala, to securely run computations on data in Hadoop clusters. It enables data science teams to use their preferred data science packages to run experiments with on-demand access to compute resources. Models can be trained, deployed, and managed centrally for increased agility and compliance.

Built for the enterprise, Cloudera Data Science Workbench includes direct integration with the Cloudera platform for a complete machine learning workflow that supports collaborative development, and can run both in the public cloud and on-premises.

Demo –¬†Watch this video for a quick 3 minute demo of Cloudera Data Science Workbench:¬†CDSW Quickstart

Typical Machine Learning Project Workflow

Machine learning is a discipline that uses computer algorithms to extract useful knowledge from data. There are many different types of machine learning algorithms, and each one works differently. In general however, machine learning algorithms begin with an initial hypothetical model, determine how well this model fits a set of data, and then work on improving the model iteratively. This training process continues until the algorithm can find no additional improvements, or until the user stops the process.

A typical machine learning project will include the following high-level steps that will transform a loose data hypothesis into a model that serves predictions.

  1. Explore and experiment with and display findings of data
  2. Deploy automated pipelines of analytics workloads
  3. Train and evaluate models
  4. Deploy models as REST APIs to serve predictions

With Cloudera Data Science Workbench, you can deploy the complete lifecycle of a machine learning project from research to deployment.