How To Do Post-Production with Abhilash Pattnaik - ML 062

How To Do Post-Production with Abhilash Pattnaik - ML 062

If you’re feeling a little nervous about your baby leaving the nest, we get it. In this episode, Ben and Daniel talk with Abhilash Pattnaik, where they discuss the ONE fact about ML you can’t forget, the do’s and don’ts about applying alerts, and the often-forgotten truth about data drift. “Machine learning models are not static; they are dynamic.”
- Abhilash Pattnaik In This Episode:
1) This one UNFORGETTABLE fact for anyone entering the machine learning world (especially if you have little experience)
2) What you NEED to remember about how to measure data accuracy and relevance
3) The Do’s and Don’ts of applying alerts within your models (and how NOT to make it problematic)
4) The often-ignored truth around data drift and why companies react well or poorly Sponsors Links Special Guest: Abhilash Pattnaik.Sponsored By:

Advertising Inquiries: https://redcircle.com/brands

Privacy & Opt-Out: https://redcircle.com/privacy

Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Episoder(209)

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Integrating Business Needs and Technical Skills in Effective Model Serving Deployments - ML 184

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Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

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24 Jan 202555min

Cows, Camels, and the Human Brain - ML 182

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What do cows and camels have to do with the human brain? The latest developments in machine learning, of course! In this episode, Michael and Ben dive into a new white paper from Facebook AI researche...

9 Jan 202542min

A/B Testing with ML ft. Michael Berk - ML 181

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2 Jan 202545min

Navigating Build vs. Buy Decisions in Emerging AI Technologies - ML 180

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26 Des 202431min

Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

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19 Des 202454min

Combating Burnout in Machine Learning: Strategies for Balance and Collaboration - ML 178

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In this episode, Ben and Michael explore burnout, particularly in machine learning and data science. They highlight that burnout stems from exhaustion, cynicism, and inefficiency and can be caused by ...

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