Model Experiments, Tracking, and Registration Using MLflow on Databricks and StreamSets

Learn how StreamSets, a modern data integration platform for DataOps, can help expedite operations at some of the most crucial stages of machine learning lifecycle and MLOps.

Data Acquisition and Preparation

Machine learning models are only as good as the quality of data and the size of datasets used to train the models. Data has shown that data scientists spend around 80% of their time on preparing and managing data for analysis and 57% of the data scientists regard cleaning and organizing data as the least enjoyable part of their work. This further validates the idea of  MLOps and the need for collaboration between data scientists and data engineers.

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