Description
Many organizations using machine learning are facing challenges storing and versioning their complex ML data as well as a large number of models generated from those data. To simplify this process, organizations tend to start building their customized ‘ML platforms.’ However, even such platforms are limited to only a few supported algorithms and they tend to be strongly coupled with companies’ internal infrastructures. MLflow, an open-source project designed to standardize and unify the machine learning process, and Delta Lake, an open-source storage layer that brings reliability to data lakes. Both originated from Databricks, can be used together to provide a reliable full data lineage through different machine learning life cycles. In this talk, we will give a detailed introduction to two popular features: MLflow Model Registry and Delta Lake Time Travel, as well as how they can work together to help create a full data lineage in machine learning pipelines. MLflow Model Registry provides a suite of APIs and intuitive UI for organizations to register and share new versions of models as well as perform lifecycle management on their existing models. It is seamlessly integrated wit…
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