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.