Machine Learning Engineered by Charlie You

04.20.2021

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

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.

03.30.2021

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

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.

03.23.2021

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

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.

03.16.2021

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

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.

03.09.2021

Developing Feast, the Leading Open Source Feature Store, with Willem Pienaar (Gojek, Tecton)

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.

03.02.2021

Bringing DevOps Best Practices into Machine Learning with Benedikt Koller from ZenML

Benedikt discusses common problems faced by teams putting machine learning into production, bringing over best practices from DevOps to solve them, and building ZenML, an open source MLOps framework.

02.23.2021

Starting an Independent AI Research Lab with Josh Albrecht from Generally Intelligent

Josh talks about pivoting from an AI recruiting startup to Generally Intelligent, an independent AI research lab. He also touches on how he defines general intelligence, what his lab is working on now, and how he creates the optimal research...

02.16.2021

Industrial Machine Learning and Building Tools for Data and Model Monitoring with Evidently AI Co-Founders Elena Samuylova and Emeli Dral

Elena and Emeli of Evidently AI discuss what they've learned applying ML across a wide variety of industries, including manufacturing and industrial process improvement, and then go into why they've started building tools for data and ML monitoring as well...

02.09.2021

Managing Data Science Teams and Hiring Machine Learning Engineers with Harikrishna Narayanan (YC Stealth Startup)

Harikrishna discusses what it means to be an effective data science manager and how he thinks about hiring for machine learning roles

02.02.2021

Lessons Learned From Hosting the ML Engineered Podcast (Charlie Interviewed on the ML Ops Community podcast)

I was recently interviewed by Demetrios and Vishnu from the ML Ops Community podcast. We discuss my experience working as an ML engineer and starting the podcast, lessons learned from talking to experts, and trends we've noticed in the industry.

01.19.2021

Building a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)

Pavle talks about his vision for a post-scarcity future, and how his company, Aether Bio, is using machine learning to accelerate towards that by creating and modeling enzymatic reactions.

01.05.2021

Best of ML Engineered in 2020 Part 1 - ML Engineering

My favorite clips from interviews with Josh Tobin, Shreya Shankar, Luigi Patruno, and Andreas Jansson on the topic of ML Engineering

12.22.2020

Solocast - Holiday Gratitude

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletter Follow Charlie on Twitter: https://twitter.com/CharlieYouAI Subscribe to ML Engineered: https://mlengineered.com/listen Comments? Questions? Submit them here: http://bit.ly/mle-survey Take...

12.15.2020

Music Information Retrieval at Spotify and the Future of ML Tooling with Andreas Jansson of Replicate

Andreas discusses the state of ML research for music information retrieval, the future of tools for data science and ML engineering, and Replicate, his recent project aiming to solve version control for ML models.

12.08.2020

Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"

Luigi discusses best practices for putting ML into production, how to make sure your data science efforts are actually adding business value, and what the future of building software might be ("Code 2.0").

12.01.2020

Coding Career Tactics - Salary Negotiation, Public Speaking, and Creating Your Own Luck w/ Shawn "swyx" Wang (AWS)

swyx returns to the podcast to discuss why and how you should negotiate your salary, getting started in public speaking, creating your own luck, learning in public, and much, much more!

11.24.2020

Yannic Kilcher: Explaining Papers on Youtube, Why Peer Review is Broken, and the Future of the Field

Yannic talks about starting his popular paper-explainer Youtube channel, how he reads for understanding, why the peer review process is broken, and where he thinks the AI field is going.

11.17.2020

How to Get Ahead in Machine Learning with Zak Slayback (1517 Fund)

Zak discusses topics in his book "How to Get Ahead" including setting career goals, finding mentors, and marketing yourself effectively.

11.10.2020

Why Multi-Modality is the Future of Machine Learning w/ Letitia Parcalabescu (University of Heidelberg, AI Coffee Break)

Letitia discusses multi-modal machine learning, the sub-field studying models that integrate multiple kinds of information (vision, language, etc.). She also talks about the need for effective communication of AI topics to the general public and her attempt to do so...

11.03.2020

Machine Learning Engineered

What's Machine Learning Engineered about?

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Machine Learning Engineered episodes:

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

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

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

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

Developing Feast, the Leading Open Source Feature Store, with Willem Pienaar (Gojek, Tecton)

Bringing DevOps Best Practices into Machine Learning with Benedikt Koller from ZenML

Starting an Independent AI Research Lab with Josh Albrecht from Generally Intelligent

Industrial Machine Learning and Building Tools for Data and Model Monitoring with Evidently AI Co-Founders Elena Samuylova and Emeli Dral

Managing Data Science Teams and Hiring Machine Learning Engineers with Harikrishna Narayanan (YC Stealth Startup)

Lessons Learned From Hosting the ML Engineered Podcast (Charlie Interviewed on the ML Ops Community podcast)

Building a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)

Best of ML Engineered in 2020 Part 1 - ML Engineering

Solocast - Holiday Gratitude

Music Information Retrieval at Spotify and the Future of ML Tooling with Andreas Jansson of Replicate

Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"

Coding Career Tactics - Salary Negotiation, Public Speaking, and Creating Your Own Luck w/ Shawn "swyx" Wang (AWS)

Yannic Kilcher: Explaining Papers on Youtube, Why Peer Review is Broken, and the Future of the Field

How to Get Ahead in Machine Learning with Zak Slayback (1517 Fund)

Why Multi-Modality is the Future of Machine Learning w/ Letitia Parcalabescu (University of Heidelberg, AI Coffee Break)

Moin Nadeem (MIT): The extraordinary future of natural language models

Peiyuan Liao: The 20 Year-Old Kaggle Grandmaster

Shreya Shankar: Lessons learned after a year of putting ML into production

Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production

Sanyam Bhutani: Chai Time Data Science

Devon Bernard: "If you can sell it, I can build it"

Catherine Yeo: Fairness in AI and Algorithms

Charles Yang: Machine Learning for Scientific Research

swyx (Shawn Wang): Coding Career Strategy

Solocast: Learning Machine Learning

Karthik Suresh: Advice for Computer Science Students

Jordan Dunne: What Engineers Should Know about Product and Program Management

Introducing Machine Learning Engineered