Machine Learning Engineered

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

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.

More episodes

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

Tue, 20 Apr 2021
31 - A Practical Approach to Learning Machine Learning with Radek Osmulski (Earth Species Project)

Tue, 30 Mar 2021
30 - From Data Science Leader to ML Researcher with Rodrigo Rivera (Skoltech ADASE, Samsung NEXT)

Tue, 23 Mar 2021
29 - The Future of ML and AI Infrastructure and Ethics with Dan Jeffries (Pachyderm, AI Infrastructure Alliance)

Tue, 16 Mar 2021
28 - Developing Feast, the Leading Open Source Feature Store, with Willem Pienaar (Gojek, Tecton)

Tue, 09 Mar 2021
27 - Bringing DevOps Best Practices into Machine Learning with Benedikt Koller from ZenML

Tue, 02 Mar 2021
26 - Starting an Independent AI Research Lab with Josh Albrecht from Generally Intelligent

Tue, 23 Feb 2021
25 - Industrial Machine Learning and Building Tools for Data and Model Monitoring with Evidently AI Co-Founders Elena Samuylova and Emeli Dral

Tue, 16 Feb 2021
24 - Managing Data Science Teams and Hiring Machine Learning Engineers with Harikrishna Narayanan (YC Stealth Startup)

Tue, 09 Feb 2021
23 - Lessons Learned From Hosting the ML Engineered Podcast (Charlie Interviewed on the ML Ops Community podcast)

Tue, 02 Feb 2021
22 - Building a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)

Tue, 19 Jan 2021
21 - Best of ML Engineered in 2020 Part 1 - ML Engineering

Tue, 05 Jan 2021
20 - Solocast - Holiday Gratitude

Tue, 22 Dec 2020
19 - Music Information Retrieval at Spotify and the Future of ML Tooling with Andreas Jansson of Replicate

Tue, 15 Dec 2020
18 - Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"

Tue, 08 Dec 2020
17 - Coding Career Tactics - Salary Negotiation, Public Speaking, and Creating Your Own Luck w/ Shawn "swyx" Wang (AWS)

Tue, 01 Dec 2020
16 - Yannic Kilcher: Explaining Papers on Youtube, Why Peer Review is Broken, and the Future of the Field

Tue, 24 Nov 2020
15 - How to Get Ahead in Machine Learning with Zak Slayback (1517 Fund)

Tue, 17 Nov 2020
14 - Why Multi-Modality is the Future of Machine Learning w/ Letitia Parcalabescu (University of Heidelberg, AI Coffee Break)

Tue, 10 Nov 2020
13 - Moin Nadeem (MIT): The extraordinary future of natural language models

Tue, 03 Nov 2020
12 - Peiyuan Liao: The 20 Year-Old Kaggle Grandmaster

Tue, 27 Oct 2020
11 - Shreya Shankar: Lessons learned after a year of putting ML into production

Tue, 20 Oct 2020
10 - Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production

Tue, 13 Oct 2020
9 - Sanyam Bhutani: Chai Time Data Science

Tue, 06 Oct 2020
8 - Devon Bernard: "If you can sell it, I can build it"

Tue, 29 Sep 2020
7 - Catherine Yeo: Fairness in AI and Algorithms

Tue, 22 Sep 2020
6 - Charles Yang: Machine Learning for Scientific Research

Tue, 15 Sep 2020
5 - swyx (Shawn Wang): Coding Career Strategy

Tue, 08 Sep 2020
4 - Solocast: Learning Machine Learning

Tue, 01 Sep 2020
3 - Karthik Suresh: Advice for Computer Science Students

Tue, 01 Sep 2020
2 - Jordan Dunne: What Engineers Should Know about Product and Program Management

Tue, 01 Sep 2020
1 - Introducing Machine Learning Engineered

Tue, 18 Aug 2020

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