A host of modern day organizations who use machine learning tend to face particular challenges when it comes to versioning and storing their complex ML data and an expansive number of models generated through it. For simplifying this process, many of them opt to build their tailored ML platforms. Nevertheless, such platforms are also limited to just a few supported algorithms. Moreover, they usually are coupled with the internal infrastructures of other firms. MLflow and Delta Lake can essentially be used by such firms to offer a dependable full data lineage with the help of diverse ML learning cycles. MLflow is an open source project meant to unify and standardize the ML process, while on the other hand, Delta Lake is an open source storage layer focused on bringing reliability to data lakes. Both of them originate from Databricks.
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, and can be used together to provide a reliable full data lineage through different machine learning life cycles.
Tracking via MLflow Model Registry
Many data scientists tend to have their entire modeling process sorted, and may even deploy ML models into production with the usage of MLflow. Experimenting with MLflow tracking and promoting models with the usage of MLflow Model Registry has also become common. MLflow Model Registry offers an intuitive UI and a suite of APIs for companies to register and share new versions of models, while also performing lifecycle management on the existing ones. Being integrated in a seamless manner with the existing MLflow tracking component, facilitates the procedure of tracing back the original where the model artifacts were generated to provide a complete lineage of the lifecycle for all models. Integrating it with the existing ML pipelines with the aim of deploying the latest version of a model to production is also possible.
Usage of Delta Lake
Data scientists usually are content with using MLflow Model Registry for MLflow tracking and more, owing to their reproducibility factor. This approach enables them to track data location, cluster set-up and code version with ease. However, data scientists should also try to search for ways to reduce time spent on data exploration, and see the exact data version used for development. Both of these elements are important to the ML development and deployment process, but coming up with a perfect solution for them can be quite a challenge. Fortunately, a few of those scalability issues can be solved by using Delta Lake. Delta Lake shall not require data scientists to change the manner of their working or learn new APIs to experience its benefits.
Features do not match with the ones used to develop
Code reproducibility for certain features is among the major challenges faced by the data scientists. This challenge can be remedied by using the Databricks Feature Store. Any kind of development of features done on Delta tables can be logged to the Feature Store with ease. It is fully underpinned by Delta and aids in keeping track of the version and code from where they were developed. It also offers added governance on the features, including look-up logic that makes features more usable and findable.
On the whole, Delta Lake can augment the odds of success of a data science and ML project.