MLA 021 Databricks

Machine Learning Guide

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Raybeam and Databricks: Ming Chang from Raybeam discusses Raybeam's focus on data science and analytics, and how their recent acquisition by Dept Agency has expanded their scope into ML Ops and AI. Raybeam often utilizes Databricks due to its comprehensive nature.
Understanding Databricks: Contrary to initial assumptions, Databricks is not just an analytics platform like Tableau but an ML Ops platform competing with tools like SageMaker and Kubeflow. It offers functionalities for creating notebooks, executing Python code, and using a hosted Spark cluster and Delta Lake for data storage.
Choosing the Right MLOps Tool: Depending on client requirements, Raybeam might recommend different tools. Decision factors include client's existing expertise, infrastructure needs, and scaling challenges. Databricks is often recommended for its ease of use and features.
Databricks Features: Offers a hosted solution for Spark clusters on AWS, Azure, or GCP; integrates with IDEs like VSCode through Databricks Connect; provides a unique Git integration for version control of notebooks; and utilizes Delta Lake for version control of Parquet files, enhancing operations like edit and delete.
Parquet and Delta Lake: Parquet files are optimized for big data, and Delta Lake provides transaction-like operations over Parquet by maintaining version history.
Pricing and Usage: Databricks adds a nominal fee on top of cloud provider charges. It's accessible for single developers and startups, making it suitable for various scales of operations.
Ming Chang's Picks: Discusses interests in automated stock trading projects and building drones with Raspberry Pi, highlighting the intersection of programming and physical computing.

Additional Resources

For a hands-on look at Ming Chang's drone project, follow his developments or connect for insights on building a Raspberry Pi-powered drone.

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