Charles discusses the breakthrough results ML has produced in scientific research, how both traditional scientists and ML researchers can get involved, and gives an unexpected answer to a rapid fire question.

Charles Yang: Machine Learning for Scientific Research

This podcast helps Machine Learning Engineers become the best at what they do. Join host Charlie You every week as he talks to the brightest minds in data science, artificial intelligence, and software engineering to discover how they bring cutting edge research out of the lab and into products that people love. You'll learn the skills, tools, and best practices you can use to build better ML systems and accelerate your career in this flourishing new field.

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Alex discusses his background working at the NSA and then starting Harvest.ai, which was acquired by and integrated into AWS. He then goes into his latest venture, Gretel.ai, which provides tools for creating anonymized and synthetic datasets.

Diving Deep into Synthetic Data with Alex Watson of Gretel.ai

Radek details his journey switching careers into software engineering and then into machine learning. He talks about mistakes he made, how he would do it now, and gives a preview of his forthcoming book.

A Practical Approach to Learning Machine Learning with Radek Osmulski (Earth Species Project)

Rodrigo details his journey from selling a company to leading data science teams at top companies to researching machine learning. He also touches on his research interests in time series data and topological data analysis.

From Data Science Leader to ML Researcher with Rodrigo Rivera (Skoltech ADASE, Samsung  NEXT)

Dan discusses why he's so excited about the future of machine learning, where it is on the technology adoption curve, the rise of a "canonical stack" of AI infrastructure, and practically approaching the hard problems in AI ethics.

The Future of ML and AI Infrastructure and Ethics with Dan Jeffries (Pachyderm, AI Infrastructure Alliance)

Willem discusses his experience building the ML platform at Gojek and what he's learned from developing and open-sourcing Feast. He also goes into his vision for it's future and how teams can best get started adopting it.

Charles Yang: Machine Learning for Scientific Research

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