Radek Osmulski is a fully self-taught machine learning engineer. After getting tired of his corporate job, he taught himself programming and started a new career as a Ruby on Rails developer. He then set out to learn machine learning. Since then, he's been a Fast AI International Fellow, become a Kaggle Master, and is now an AI Data Engineer on the Earth Species Project.

Learn more about Radek:

https://www.radekosmulski.com

https://twitter.com/radekosmulski

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Timestamps:

02:15 How Radek got interested in programming and computer science

09:00 How Radek taught himself machine learning

26:40 The skills Radek learned from Fast AI

39:20 Radek's recommendations for people learning ML now

51:30 Why Radek is writing a book

01:01:20 Radek's work at the Earth Species Project

01:10:15 How the ESP collects animal language data

01:21:05 Rapid fire questions

Links:

Radek's Book "Meta-Learning"

Andrew Ng ML Coursera

Fast AI

Universal Language Model Fine-tuning for Text Classification

How to do Machine Learning Efficiently

NPR - Two Heartbeats a Minute

Earth Species Project

A Guide to the Good Life

The Origin of Wealth

Make Time

You Are Here

Rodrigo Rivera is a machine learning researcher at the Advanced Data Analytics in Science and Engineering Group at Skoltech and technical director of Samsung Next. He's previously been in data science and research leadership roles at companies all around the world including Rocket Internet and Philip-Morris.

Learn more about Rodrigo:

https://rodrigo-rivera.com/

https://twitter.com/rodrigorivr

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletter

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Timestamps:

03:00 How Rodrigo got started in computer science and started his first company

10:40 Rodrigo's experiences leading data science teams at Rocket Internet and PMI

26:15 Leaving industry to get a PhD in machine learning

28:55 Data science collaboration between business and academia

32:45 Rodrigo's research interest in time series data

39:25 Topological data analysis

45:35 Framing effective research as a startup

48:15 Neural Prophet

01:04:10 The potential future of Julia for numerical computing

01:08:20 Most exciting opportunities for ML in industry

01:15:05 Rodrigo's advice for listeners

01:17:00 Rapid fire questions

Links:

Rodrigo's Google Scholar

Advanced Data Analytics in Science and Engineering Group

Neural Prophet

M-Competitions

Machine Learning Refined

Foundations of Machine Learning

A First Course in Machine Learning

Dan Jeffries is the chief technical evangelist at Pachyderm, a leading data science platform. He's a prominent writer and speaker on all things related to the future. He's been in software for over two decades, many of those at Redhat, and is the founder of the AI Infrastructure Alliance and Practical AI Ethics.

Learn more about Dan:

https://twitter.com/Dan_Jeffries1

https://medium.com/@dan.jeffries

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/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

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Timestamps:

02:15 How Dan got started in computer science

06:50 What Dan is most excited about in AI

14:45 Where we are in the adoption curve of ML

20:40 The "Canonical Stack" of ML

32:00 Dan's goal for the AI Infrastructure Alliance

40:55 "Problems that ML startups don't know they're going to have"

49:00 Closed vs open source tools in the Canonical Stack

01:00:05 Building out the "boring" part of the infrastructure to enable exciting applications

01:08:40 Dan's practical approach to AI Ethics

01:23:50 Rapid fire questions

Links:

Pachyderm

AI Infrastructure Alliance

Practical AI Ethics Alliance

Rise of the Canonical Stack in Machine Learning

Rise of AI - The Age of AI in 2030

Google Magenta

AlphaGo Documentary

Thinking in Bets

A History of the World in 6 Glasses

Super-Thinking

Willem Pienaar is the co-creator of Feast, the leading open source feature store, which he leads the development of as a tech lead at Tecton. Previously, he led the ML platform team at Gojek, a super-app in Southeast Asia.

Learn more:

https://twitter.com/willpienaar

https://feast.dev/

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/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 the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:15 How Willem got started in computer science

03:40 Paying for college by starting an ISP

05:25 Willem's experience creating Gojek's ML platform

21:45 Issues faced that led to the creation of Feast

26:45 Lessons learned building Feast

33:45 Integrating Feast with data quality monitoring tools

40:10 What it looks like for a team to adopt Feast

44:20 Feast's current integrations and future roadmap

46:05 How a data scientist would use Feast when creating a model

49:40 How the feature store pattern handles DAGs of models

52:00 Priorities for a startup's data infrastructure

55:00 Integrating with Amundsen, Lyft's data catalog

57:15 The evolution of data and MLOps tool standards for interoperability

01:01:35 Other tools in the modern data stack

01:04:30 The interplay between open and closed source offerings

Links:

Feast's Github

Gojek Data Science Blog

Data Build Tool (DBT)

Tensorflow Data Validation (TFDV)

A State of Feast

Google BigQuery

Lyft Amundsen

Cortex

Kubeflow

MLFlow

Benedikt Koller is a self-professed "Ops guy", having spent over 12 years working in roles such as DevOps engineer, platform engineer, and infrastructure tech lead at companies like Stylight and Talentry in addition to his own consultancy KEMB. He's recently dove head first into the world of ML, where he hopes to bring his extensive ops knowledge into the field as the co-founder of Maiot, the company behind ZenML, an open source MLOps framework.

Learn more:

https://zenml.io/

https://maiot.io/

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/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 the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:15 Introducing Benedikt Koller

05:30 What the "DevOps revolution" was

10:10 Bringing good Ops practices into ML projects

30:50 Pivoting from vehicle predictive analytics to open source ML tooling

34:35 Design decisions made in ZenML

39:20 Most common problems faced by applied ML teams

49:00 The importance of separating configurations from code

55:25 Resources Ben recommends for learning Ops

57:30 What to monitor in an ML pipelines

01:00:45 Why you should run experiments in automated pipelines

01:08:20 The essential components of an MLOps stack

01:10:25 Building an open source business and what's next for ZenML

01:20:20 Rapid fire questions

Links:

ZenML's GitHub

Maiot Blog

The Twelve Factor App

12 Factors of reproducible Machine Learning in production

Seldon

Pachyderm

KubeFlow

Something Deeply Hidden

The Expanse Series

The Three Body Problem

Extreme Ownership

Josh Albrecht is the co-founder and CTO of Generally Intelligent, an independent research lab investigating the fundamentals of learning across humans and machines. Previously, he was the lead data architect at Addepar, CTO of CloudFab, and CTO of Sourceress, which Generally Intelligent is a pivot from.

Learn more about Josh:

http://joshalbrecht.com/

http://generallyintelligent.ai/

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/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 the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:15 Introducing Josh Albrecht

03:30 How Josh got started in computer science

06:35 Josh's first two startup attempts

09:15 The tech behind Sourceress, an AI recruiting platform

16:10 Pivoting from Sourceress to Generally Intelligent, an AI research lab

23:50 How Josh defines "general intelligence"

28:35 Why Josh thinks self-supervised learning is the current most promising research area

36:15 Generally Intelligent's immediate research roadmap: BYOL, simulated environments

59:20 How Josh thinks about creating an optimal research environment

01:11:35 The "why" behind starting an independent research lab

01:13:30 AI alignment

01:17:00 Rapid fire questions

Links:

Bootstrap your own latent: A new approach to self-supervised Learning

Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)

BYOL works even without batch statistics

Generally Intelligent Podcast

Consequences of Misaligned AI

Why We Sleep

Peak

Elena Samuylova and Emeli Dral are the co-founders of Evidently AI, where they build open source tools to analyze and monitor machine learning models. Elena was previously the head of the startup ecosystem at Yandex, director of business development at their data factory and chief product officer at Mechanica AI. Emeli was previously a data scientist at Yandex, chief data scientist at the data factory and Mechanica AI in addition to teaching machine learning both online and at multiple universities.

Learn more about Elena, Emeli, and Evidently AI:

https://evidentlyai.com/

https://twitter.com/elenasamuylova

https://twitter.com/EmeliDral

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/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 the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:15 How Emeli and Elena each got started in data science

07:10 Applying machine learning across a wide variety of industries at the Yandex Data Factory

14:55 Using ML for industrial process improvement

23:35 Challenges encountered in industrial ML and technical solutions

27:15 The huge opportunity for ML in manufacturing

34:35 How to ensure safety when using models in physical systems

37:40 Why they started working on tools for data and ML monitoring

42:50 Different kinds of data drift and how to address them

48:25 Common mistakes ML teams make in monitoring

55:25 Features of Evidently AI's library

57:35 Building open source software

01:02:25 Technical roadmap for Evidently

01:05:50 Monitoring complex data

01:08:50 Business roadmap for Evidently

01:11:35 Rapid fire questions

Links:

Evidently on Github

Evidently AI's Blog

Thinking Fast and Slow

Flow

Doing Good Better