Building Machine Learning Workflows: Lessons Learned

Matei Zaharia, discusses the development of the Databricks platform and the challenges of building effective machine learning workflows. He emphasizes the importance of validation and use case analysis when adding new concepts to a project. He recalls adding concepts one by one, and ensuring that they were thoroughly tested and validated for potential users. He also highlights the importance of keeping the number of concepts low and the platform simple, which makes it easier for people to learn and use. The development of the model registry was a key moment for him and his team, which allowed for versioning and review of proposed new models. Overall, His experience provides valuable insights into the process of building effective machine learning workflows, emphasizing the importance of user validation and simplicity in design.

MLOps Coffee Sessions #155 with Matei Zaharia, The Birth and Growth of Spark: An Open Source Success Story, co-hosted by Vishnu Rachakonda.

// Abstract
We dive deep into the creation of Spark, with the creator himself - Matei Zaharia Chief technologist at Databricks. This episode also explores the development of Databricks' other open source home run ML Flow and the concept of "lake house ML". As a special treat Matei talked to us about the details of the "DSP" (Demonstrate Search Predict) project, which aims to enable building applications by combining LLMs and other text-returning systems.

// About the guest:
Matei has the unique advantage of being able to see different perspectives, having worked in both academia and the industry. He listens carefully to people's challenges and excitement about ML and uses this to come up with new ideas. As a member of Databricks, Matei also has the advantage of applying ML to Databricks' own internal practices. He is constantly asking the question "What's a better way to do this?"

// Bio
Matei Zaharia is an Associate Professor of Computer Science at Stanford and Chief Technologist at Databricks. He started the Apache Spark project during his Ph.D. at UC Berkeley, and co-developed other widely used open-source projects, including MLflow and Delta Lake, at Databricks. At Stanford, he works on distributed systems, NLP, and information retrieval, building programming models that can combine language models and external services to perform complex tasks. Matei’s research work was recognized through the 2014 ACM Doctoral Dissertation Award for the best Ph.D. dissertation in computer science, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).

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