Maintaining Backward Compatibility in Software Projects: Strategies from Industry Experts - ML 164

Maintaining Backward Compatibility in Software Projects: Strategies from Industry Experts - ML 164

Today, host Michael Berk and Ben Wilson dive deep into the multifaceted world of software engineering and data science with their insightful guest, Sandy Ryza a lead engineer from Dagster Labs. In this episode, they explore a range of intriguing topics, from the impact of the broken windows theory on code quality to the delicate balance of maintaining backward compatibility in evolving software projects.
Sandy talks about the challenges and learnings in transitioning from data science back to software engineering, including dependency management and designing for diverse use cases. They touch on the importance of clear naming conventions, tooling, and infrastructure enforcement to maintain high code quality. Plus, they discuss the intricate process of selecting and managing Python libraries, the satisfaction of refactoring old code, and the necessity of balancing new feature development with stability.
Michael and Ben will guide us through these essential discussions, emphasizing the significance of user-centric API design and the benefits of open source software. They also get practical advice on navigating API changes and managing dependencies effectively, with real-world examples from Dagster, Spark Time Series, and the libraries Numba and Pydantic.
Join them for an episode packed with valuable insights and strategies for becoming a top-end developer! Don’t forget to follow Sandy on Twitter and check out Dagster.io for more information on his work.

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How to Create Team Utils - ML 122

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Have you ever written code and thought, "hmm, I wonder if my teammates would use this." Well in today's episode, we show you how to go from concept to production-level code. Spoiler alert: you're goin...

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How to Get Sh*t Done - ML 121

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In today's episode, Michael and Ben break down some surefire methods to be successful. If you follow these tips, you are guaranteed to co-found the next Google. Some topics include time boxing excitin...

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ML at Netflix and How to Learn Deeply - ML 120

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In today's episode, we speak with Netflix ML engineer Amir Ziai. Expect to learn about building ML tools for stakeholders, the pros and cons of a Netflix-like culture, and Amir's strategy for learning...

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How to get Promoted - ML 119

How to get Promoted - ML 119

In today's episode, we dive into Ben's experience in navigating the career ladder. Expect to learn why your leveling matrix is probably wrong and how you should actually spend your time to maximize ca...

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How does Search Work? - ML 118

How does Search Work? - ML 118

In today's episode, we speak with Roman Grebennikov, an expert in ranking algorithms. Expect to learn about his open source project, the difference between retrieval and ranking, and much more!Sponsor...

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How to Learn a New Tool - ML 117

How to Learn a New Tool - ML 117

In today's episode, we walk through Ben's experience creating the Hugging Face transformer flavor for ML flow. During this case study we highlight the structure he uses to learn new technologies and c...

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The Innovation Cycle of AI - ML 116

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Today we speak with ex-Googler, Praveen Paritosh. He has over 20 years of experience as a research scientist and has worked on some of AI's most impactful projects. Expect to learn about scientific in...

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All Things Machine Learning - ML 115

All Things Machine Learning - ML 115

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