Adventures in Machine Learning
Latest Episodes
Unraveling the Complexities of Model Deployment in Dynamic Marketplaces - ML 151
Deeksha Goyal is the Senior Machine Learning Engineer at Lyft and Michael Sun is the Staff Software Engineer at Lyft. They delve into the intricacies of machine learning and data-driven technology. In
Adaptive Industry ML: Challenges, Automation, and Model Applications - ML 149
Terry Rodriguez is the Co-Founder at Remyx AI. They discuss the challenges and opportunities in deploying and updating AI models for robotics, exploring the potential applications across various indus
Harnessing Open Source Contributions in Machine Learning and Quantization - ML 148
Lukas Geiger is a Deep Learning Scientist, open-source developer, and an astroparticle physicist. He shares his experience using machine learning to analyze cosmic ray particles and detect secondary p
Data Platform Innovation: Navigating Challenges and Building a Unified Experience - ML 147
Nick Schrock is the Founder of Dagster Labs. He is also the Creator of Dagster and the Co-creator of GraphQL. They delve into the world of data engineering, software development, and ML orchestration.
The Science-Engineering Blend - ML 146
Ben and Michael dive into the dynamic relationship between engineers and scientists in the realms of software engineering and physical science. They explore the differences and similarities between th
The Impact of Process on Successful Tech Companies - ML 145
Michael and Ben dive into the critical role of design in software development processes. They emphasize the value of clear and understandable code, the importance of thorough design for complex projec
Delivering Scoped Solutions: Lessons in Fixing Production System Issues - ML 144
Michael and Ben share their insights on being called in to fix issues in production systems at the last minute. They stress the importance of asking questions to understand the context and navigate th
MLOps 101: Scoping, Latency, Data Curation, and Continuous Model Retraining - ML 143
Ben and Michael dive into the world of machine learning operations (MLOps) and discuss the complexities of building a computer vision pipeline to detect fishing boats at ports. They unpack the intrica
How to Create Team Utils - ML 122
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 going to have to write tests!SponsorsChuck&
How to Get Sh*t Done - ML 121
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 exciting work, tips for growing documentation, pitching to