Abstract: One must take a holistic view of the entire data analytics realm when it comes to planning for data science initiatives. Data engineering is a key enabler of data science, helping furnish reliable, quality data in a timely fashion. Delta Lake, an open-source storage layer that brings reliability to data lakes, can help take your data reliability to the next level. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
In this session you will learn about:
* The data science lifecycle
* The importance of data engineering to successful data science
* Key tenets of modern data engineering
* How Delta Lake can help make reliable data ready for analytics
* The ease of adopting Delta Lake for powering your data lake
* How to incorporate MLflow and Delta Lake within your data infrastructure to enable Data Science