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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Confidence as Data Scientist - ML 130

Confidence as Data Scientist - ML 130

In today's episode, we chat with Adam Ross Nelson - a data scientist, career mentor, and writer. Our primary focus is understanding when you've achieved "good enough" skills. Additionally, we explore ...

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A Case Study: Recommendation Engines - ML 129

A Case Study: Recommendation Engines - ML 129

In today's episode, we delve into a popular topic at Databricks: building recommendation engines. We'll guide you on how to plan such projects, measure your model's success, suggest effective algorith...

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Maximizing Efficiency in ML Project Development - ML 128

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Ben and Michael delve into the world of data science and software development. They discuss the importance of setting standards and documentation in project development, the struggle of reviewing and ...

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AI that Make You Better - ML 127

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Challenges for LLM Implementation - ML 126

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In today's episode, we speak with Anand Das, the CTO and co-founder of bito.ai, an LLM-powered code assistant. Expect to learn about managing LLM context, keeping LLMs up-to-date, common user pitfalls...

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ML in the Cannabis Industry - ML 125

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In today's episode, we speak with Shivek Sachdev, product owner at a seed-to-sale Cannabis company. Expect to learn about disrupting new industries, applying LLMs to daily work, and much more!Sponsors...

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How AI Impacts Society - ML 124

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In today's episode we speak to Eric Daimler, a White House Presidential Innovation Fellow, professor at Carnegie Mellon, and the current CEO of Conexus. Expect to learn about how AI is impacting the m...

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LLMs on Azure - ML 123

LLMs on Azure - ML 123

In today's episode, we speak with Jeff Procise, founder of Wintellect, an Azure-focused software consulting company. Expect to learn about your next LLM MVP on Azure, the societal impact of AI, the bi...

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