Niche Player: Cloudera

The Palo Alto, Calif.-based company’s Cloudera Machine Learning (CML) offering is supported by Cloudera Data Engineering and Cloudera Data Visualization. The products are interconnected and delivered as services on top of the Cloudera Data Platform. CML has replaced Cloudera’s on-premises Cloudera Data Science Workbench platform to provide multi-cloud capabilities. Cloudera focuses on unifying ML workflows across data warehousing, data engineering, DSML and operationalization. Cloudera ranks among the bottom of the pack on Gartner’s Magic Quadrant for both vision and execution.

Strengths: Cloudera aims to overcome the overhead associated with managing Spark clusters and dependencies by maintaining containerized, repeatable workflows that can be scaled on demand. CML enables data science teams to use a variety of ML runtimes without prescribing underlying frameworks.

Weaknesses: The majority of DSML tasks undertaken in CML require coding and use of open-source libraries in Python, R, Scala and similar languages with no visual workflow interface. Gartner said there’s little augmentation in the platform to help citizen data scientists build their own models.