Machine Learning Engineered

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

Charlie You — Tue, 20 Apr 2021 05:00:00 -0500

Alex Watson is the co-founder and CEO of Gretel.ai, a startup that offers APIs for creating anonymized and synthetic datasets. Previously he was the founder of Harvest.ai, whose product Macie, an analytics platform protecting against data breaches, was acquired by AWS.

Learn more about Alex and Gretel AI:

http://gretel.ai

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Timestamps:

02:15 Introducing Alex Watson

03:45 How Alex was first exposed to programming

05:00 Alex's experience starting Harvest AI, getting acquired by AWS, and integrating their product at massive scale

21:20 How Alex first saw the opportunity for Gretel.ai

24:20 The most exciting use-cases for synthetic data

28:55 Theoretical guarantees of anonymized data with differential privacy

36:40 Combining pre-training with synthetic data

38:40 When to anonymize data and when to synthesize it

41:25 How Gretel's synthetic data engine works

44:50 Requirements of a dataset to create a synthetic version

49:25 Augmenting datasets with synthetic examples to address representation bias

52:45 How Alex recommends teams get started with Gretel.ai

59:00 Expected accuracy loss from training models on synthetic data

01:03:15 Biggest surprises from building Gretel.ai

01:05:25 Organizational patterns for protecting sensitive data

01:07:40 Alex's vision for Gretel's data catalog

01:11:15 Rapid fire questions

Links:

Gretel.ai Blog

NetFlix Cancels Recommendation Contest After Privacy Lawsuit

Greylock - The Github of Data

Improving massively imbalanced datasets in machine learning with synthetic data

Deep dive on generating synthetic data for Healthcare

Gretel’s New Synthetic Performance Report

The Martian

Snow Crash

The MurderBot Diaries

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

Charlie You — Tue, 30 Mar 2021 09:00:00 -0500

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

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

Charlie You — Tue, 23 Mar 2021 05:00:00 -0500

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

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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

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

Charlie You — Tue, 16 Mar 2021 05:00:00 -0500

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

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Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

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

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

Charlie You — Tue, 09 Mar 2021 05:00:00 -0500

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

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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)

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

Charlie You — Tue, 02 Mar 2021 05:00:00 -0500

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/

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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

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

Charlie You — Tue, 23 Feb 2021 05:00:00 -0500

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

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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

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

Charlie You — Tue, 16 Feb 2021 05:00:00 -0500

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

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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

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

Charlie You — Tue, 09 Feb 2021 05:00:00 -0500

Harikrishna Narayanan is the co-founder of a YC-backed stealth startup. He was previously a Principal Engineer at Yahoo, a Director in Workday's Machine Learning organization, and holds an M.S. from Georgia Tech.

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

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Timestamps:

02:45 How Hari got started in computer science and machine learning

06:00 Making the transition from IC to manager

14:35 What it means to be an effective engineering manager

19:20 Differences in managing machine learning vs traditional software teams

24:30 The importance of explaining complicated topics simply

30:15 How he thinks about hiring for data science and machine learning

36:50 Mistakes Workday made as it adopted machine learning

41:50 Essential skills for machine learning engineers

54:05 Why the future of AI is augmentation, not automation

58:30 His experience so far with YC

01:02:00 Rapid fire questions

Links:

Growth Mindset

The Feynman Technique

Radical Candor

Trillion Dollar Coach

The Hard Thing About Hard Things

Sapiens

A Short History of Nearly Everything

On Intelligence

Prediction Machines

Algorithms to Live By

The Synthetic Data Vault

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

Charlie You — Tue, 02 Feb 2021 05:00:00 -0500

Learn more about the ML Ops Community: https://mlops.community/

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

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Timestamps:

02:45 Intro

04:10 How I got into data science and machine learning

08:25 My experience working as an ML engineer and starting the podcast

12:15 Project management methods for machine learning

20:50 ML job roles are trending towards more specialization

26:15 ML tools enable collaboration between roles and encode best practices

34:00 Data privacy, security, and provenance as first class considerations

39:30 The future of managed ML platforms and cloud providers

49:05 What I've learned about building a career in ML engineering

54:10 Dealing with information overload

Links:

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

The Third Wave Data Scientist

Practical ML Ops // Noah Gift // MLOps Coffee Sessions

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

SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions

Luigi Patruno on the ML Ops Community podcast

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

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

Charlie You — Tue, 19 Jan 2021 05:00:00 -0500

Pavle Jeremic is the founder and CEO of Aether Biomachines, one of the most exciting ML-powered startups I've come across. His mission is to solve scarcity and Aether is the first step towards that. He was recently featured in Forbes' 30 under 30 in Manufacturing and holds a B.S. in Biomolecular Engineering from UC Santa Cruz.

Learn more:

Aether Biomachines

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

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Timestamps:

02:45 Pavle Jeremic

05:20 How Pavle was introduced to computer science and programming

08:00 Solving scarcity from first principles

23:20 How Aether contributes to the post-scarcity future

29:30 What enzymatic reaction data looks like

37:20 Using deep learning to figure out what enzymatic experiments to run next

39:45 How Aether runs thousands of experiments at a time

47:00 What the current bottleneck of the system is

53:15 The evolution of ML models at Aether

59:00 Gaps in existing ML infrastructure solutions

01:03:30 Why Aether is releasing some of their data for a competition

01:06:50 The upcoming roadmap for Aether

01:09:30 Rapid fire questions

Links:

Founders First Interview - Making Alchemy Real

DeepChem

Engines of Creation

Rama Series

Best of ML Engineered in 2020 Part 1 - ML Engineering

Charlie You — Tue, 05 Jan 2021 05:00:00 -0500

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

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Timestamps:

02:50 Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production

21:48 Shreya Shankar: Lessons learned after a year of putting ML into production

34:44 Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"

53:28 Music Information Retrieval at Spotify and the Future of ML Tooling with Andreas Jansson of Replicate

Solocast - Holiday Gratitude

Charlie You — Tue, 22 Dec 2020 05:00:00 -0500

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

Charlie You — Tue, 15 Dec 2020 05:00:00 -0500

Andreas Jansson is the co-founder of Replicate, a version control tool for machine learning. He holds a PhD from City University of London in Music Informatics and was previously a machine learning engineer at Spotify, researching and applying algorithms for music information retrieval.

Learn more about Andreas:

https://replicate.ai/

https://www.linkedin.com/in/janssonandreas/

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

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Timestamps:

02:30 Andreas Jansson

07:30 Overview of music information retrieval (MIR)

13:30 Why use spectrograms and not raw audio?

19:55 The potential for transformers in MIR

22:45 Most exciting applications for ML in MIR

29:20 Challenges in putting ML into production

36:45 What Andreas imagines for the future of ML tools

41:45 Why he's building a tool for ML version control (http://replicate.ai/)

52:55 What Replicate enables via integration or as a platform

01:02:55 Learnings from doing customer discovery for Replicate

01:14:10 "Github for ML models and data"

01:22:30 Rapid fire questions

Links:

WaveNet: a generative model for raw audio

Singing Voice Separation with Deep U-Net CNNs

Joint Singing Voice Separation and F0 Estimation with Deep U-Net Architectures

arXiv Vanity

Replicate

Replicate's Discord

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

Charlie You — Tue, 08 Dec 2020 05:00:00 -0500

Luigi is the director of data science at 2U, where he leads a team in developing ML models and infrastructure to predict student success outcomes. He's also the founder of ML in Production, a blog and newsletter that helps readers build, deploy, and run ML systems.

Learn more about Luigi:

https://mlinproduction.com/

https://twitter.com/mlinproduction

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

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Comments? Questions? Submit them here: http://bit.ly/mle-survey

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Timestamps:

02:45 Luigi Patruno

04:50 How can ML teams be more rigorous in their engineering practices?

10:25 Best practices for monitoring and logging ML systems

18:00 Adding business value with data science

37:10 Most valuable types of tools for ML in production

43:15 What an ideal data pipeline setup looks like

47:50 Unbundling the "Data Scientist" role

50:35 The future of building software: "Code 2.0"

59:45 Most valuable skills for the future

01:10:15 Learnings from writing his blog "ML in Production"

01:15:00 Rapid fire questions

Links:

Luigi's interview on Datacast

Ultimate Guide to Deploying ML Models

Maximizing Business Impact with Machine Learning

Two Types of Companies Using ML

The AI Hierarchy of Needs

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

Machine Learning is Forcing Software Development to Evolve

ML Street Talk #29: GPT-3, Prompt Engineering, Trading, AI Alignment, Intelligence

Building Machine Learning Powered Applications

How to Change Your Mind

The War of Art

Sapiens

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

Charlie You — Tue, 01 Dec 2020 05:00:00 -0500

Shawn Wang formerly worked in finance as a derivatives trader and equity analyst before burning out and pivoting towards tech. He's a prolific blogger who goes under the pseudonym "swyx" and recently published the excellent Coding Career Handbook. He's a graduate of Free Code Camp and Full Stack Academy now working at AWS as a Senior Developer Advocate.

Learn more about Shawn:

https://swyx.io/

https://www.learninpublic.org/

https://twitter.com/swyx

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

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Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:45 swyx is back!

05:25 How his book has been received so far

11:35 Why and how to negotiate your salary

24:10 Getting started in public speaking, giving talks at meetups and conferences

35:45 The role of luck in your career and how to create it

51:15 Biggest is not best, best *for me *****is best

59:20 Why swyx angel-invested in Circle

01:12:00 On Randy Pausch's Time Management lecture

01:18:00 Using open source to accelerate your coding skill

01:20:00 Handling information overload and enhancing retention with note taking

01:27:20 What swyx does in his job as a Developer Advocate and why you should consider non-coding roles

01:37:30 swyx's new podcast Career Chats (https://careerchats.transistor.fm/)

Links:

swyx's first ML Engineered appearance

swyx's book Coding Career Handbook

How to Create Luck

Notes on Time Management from a Dying Professor

Building a Second Brain

SimpleNote

swyx's new podcast with Randall Kanna "Career Chats"

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

Charlie You — Tue, 24 Nov 2020 05:00:00 -0500

Yannic Kilcher is PhD candidate at ETH Zurich researching deep learning, structured learning, and optimization for large and high-dimensional data. He produces videos on his enormously popular Youtube channel breaking down recent ML papers.

Follow Yannic on Twitter: https://twitter.com/ykilcher

Check out Yannic's excellent Youtube channel: https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew

Listen to the ML Street Talk podcast: https://podcasts.apple.com/us/podcast/machine-learning-street-talk/id1510472996

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

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Timestamps:

02:40 Yannic Kilcher

07:05 Research for his PhD thesis and plans for the future

12:05 How he produces videos for his enormously popular Youtube channel

21:50 Yannic's research process: choosing what to read and how he reads for understanding

27:30 Why ML conference peer review is broken and what a better solution looks like

45:20 On the field's obsession with state of the art

48:30 Is deep learning is the future of AI? Is attention all you need?

56:10 Is AI overhyped right now?

01:01:00 Community Questions

01:13:30 Yannic flips the script and asks me about what I do

01:25:30 Rapid fire questions

Links:

Yannic's amazing Youtube Channel

Yannic's Google Scholar

Yannic's Community Discord Channel

On the Measure of Intelligence: arXiv paper and Yannic's video series

How I Read a Paper: Facebook's DETR (Video Tutorial)

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)

Zero to One

The Gulag Archipelago

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

Charlie You — Tue, 17 Nov 2020 05:00:00 -0500

Zak Slayback is a principal at 1517 Fund, a venture capital fund that prioritizes working with dropouts. He wrote the excellent book "How to Get Ahead", one of my most recommended books on careers, and runs Get Ahead Labs where he teaches how to write outstanding cold emails.

Learn more about Zak:

https://zakslayback.com/

https://www.1517fund.com/

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

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Timestamps:

02:35 Zak Slayback

04:45 Using opportunity cost, signaling theory, and incentives to accelerate your career (https://zakslayback.com/frameworks-success-opportunity-cost/)

14:35 How to set career goals (https://zakslayback.com/ambition-mapping/)

20:15 Rene Girard and Mimetic Desire

24:30 The difference between a mentor, a coach/consultant, and an advisor (https://zakslayback.com/whats-difference-mentors-advisors-coaches/)

35:40 Finding a mentor (https://zakslayback.com/professional-mentor-dream-job/)

44:30 Fighting mental blocks against reaching out to potential mentors

47:30 Why you should start a personal website (https://zakslayback.com/why-start-a-website/)

56:15 What the most important "meta-skills" are and how to stack talents

01:05:35 Most over-looked sections of the book

01:09:00 The future of higher education: the new 95 theses from 1517 Fund (https://medium.com/1517/a-new-95-ec071200d98f)

01:23:05 What Zak thinks the most exciting trends in technology are

01:35:15 Rapid fire questions

Links:

The End of School and Building a Valuable Skillset with Zak Slayback

Deschool Yourself and Find Your Focus – With Zak Slayback

Zak's book - How to Get Ahead (highly recommended!)

Ambition Mapping

Rene Girard and Mimetic Desire

Why Tacit Knowledge is More Important Than Deliberate Practice

How to Get Your Dream Job and Mentor in 6 Easy Steps

What’s The Difference Between Mentors, Advisors, and Coaches?

How to Get Ahead When You Have Nothing to Offer

“Why Should I Start a Website?”

Frameworks for Making Better Decisions: Opportunity Cost

How to Fail at Almost Everything and Still Win Big

The New 95 Theses

Antifragile

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

Charlie You — Tue, 10 Nov 2020 05:00:00 -0500

Letitia Parcalabescu is a PhD candidate at the University of Heidelberg focused on multi-modal machine learning, specifically with vision and language.

Learn more about Letitia:

https://www.cl.uni-heidelberg.de/~parcalabescu/

https://www.youtube.com/channel/UCobqgqE4i5Kf7wrxRxhToQA

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

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Comments? Questions? Submit them here: http://bitly.com/mle-survey

Timestamps:

01:30 Follow Charlie on Twitter (https://twitter.com/CharlieYouAI)

02:40 Letitia Parcalabescu

03:55 How she got started in CS and ML

07:20 What is multi-modal machine learning? (https://www.youtube.com/playlist?list=PLpZBeKTZRGPNKxoNaeMD9GViU_aH_HJab)

16:55 Most exciting use-cases for ML

20:45 The 5 stages of machine understanding (https://www.youtube.com/watch?v=-niprVHNrgI)

23:15 The future of multi-modal ML (GPT-50?)

27:00 The importance of communicating AI breakthroughs to the general public

37:40 Positive applications of the future “GPT-50”

43:35 Letitia’s CVPR paper on phrase grounding (https://openaccess.thecvf.com/content_CVPRW_2020/papers/w56/Parcalabescu_Exploring_Phrase_Grounding_Without_Training_Contextualisation_and_Extension_to_Text-Based_CVPRW_2020_paper.pdf)

53:15 ViLBERT: is attention all you need in multi-modal ML? (https://arxiv.org/abs/1908.02265)

57:00 Preventing “modality dominance”

01:03:25 How she keeps up in such a fast-moving field

01:10:50 Why she started her AI Coffee Break YouTube Channel (https://www.youtube.com/c/AICoffeeBreakwithLetitiaParcalabescu/)

01:18:10 Rapid fire questions

Links:

AI Coffee Break Youtube Channel

Exploring Phrase Grounding without Training

AI Coffee Break series on Multi-Modal learning

What does it take for an AI to understand language?

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations

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

Charlie You — Tue, 03 Nov 2020 05:00:00 -0500

Moin Nadeem is a masters student at MIT, where he studies natural language generation. His research interests broadly include natural language processing, information retrieval, and software systems for machine learning.

Learn more about Moin:

https://moinnadeem.com/

https://twitter.com/moinnadeem

Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletter

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Timestamps:

01:35 Follow Charlie on Twitter (https://twitter.com/CharlieYouAI)

03:10 How Moin got started in computer science

05:50 Using ML to identify depression on Twitter in high school

11:00 Building a system to track phone locations on MIT’s campus

14:35 Specializing in NLP

17:20 Building an end-to-end fact-checking system (https://www.aclweb.org/anthology/N19-4014/)

25:15 Predicting statement stance with neural multi-task learning (https://www.aclweb.org/anthology/D19-6603/)

27:20 Is feature engineering in NLP dead?

29:40 Reconciling language models with existing knowledge graphs

35:20 How advances in AI hardware will affect NLP research (crazy!)

47:25 Moin’s research into sampling algorithms for natural language generation (https://arxiv.org/abs/2009.07243)

57:10 Under-rated areas of ML research

01:00:10 How research works at MIT CSAIL

01:04:35 How Moin keeps up in such a fast-moving field

01:11:30 Starting the MIT Machine Intelligence Community

01:16:30 Rapid Fire Questions

Links:

FAKTA: An Automatic End-to-End Fact Checking System

StereoSet: Measuring stereotypical bias in pretrained language models

Neural Multi-Task Learning for Stance Prediction

Rich Sutton - The Bitter Lesson

A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation

Strategies for Pre-training Graph Neural Networks

Transformers For Image Recognition at Scale

Cerebras CS-1

Klarity: AI for Law Contract Review

Peiyuan Liao: The 20 Year-Old Kaggle Grandmaster

Charlie You — Tue, 27 Oct 2020 05:00:00 -0500

Peiyuan Liao is the youngest Chinese Kaggle grandmaster at only 20 years old with numerous gold medals and 1st, 2nd, and 3rd place finishes. He helped research two deep learning papers while in high school and now researches adversarial attacks on graph neural networks at Carnegie Mellon.

Learn more about Peiyuan:

https://liaopeiyuan.github.io/

https://www.kaggle.com/alexanderliao

Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletter

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Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

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Timestamps:

03:20 How Peiyuan was first exposed to CS and ML

06:45 Researching deep learning in high school

10:30 Researching graph neural networks at Carnegie Mellon (https://arxiv.org/abs/2009.13504)

20:30 How he keeps up with the field and gets research ideas

24:05 Research tools he uses

31:30 Advice for Kaggle beginners

34:30 How Peiyuan first approaches a new Kaggle competition

40:15 His team's 3rd-place solution to the 2020 Google Landmark Recognition Challenge (https://arxiv.org/abs/2010.05350)

50:30 How he approached the Global Wheat Detection challenge (https://www.kaggle.com/c/global-wheat-detection/discussion/175961)

56:40 How he decides to quit a Kaggle competition

59:25 The difference between him and the average Kaggler

01:03:20 Contributing to open source projects

01:06:00 Rapid Fire Questions

Links:

CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders

Graph Adversarial Networks: Protecting Information against Adversarial Attacks

Peiyuan's Kaggle Profile

Open Neural Network Exchange (ONNX)

Apache TVM

Deformable Convolutional Networks

Google JAX

Robert Harper

Google Landmark Recognition 2020 Competition Third Place Solution

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Global Wheat Detection 2nd Place Solution

Lovász-Softmax loss

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

Charlie You — Tue, 20 Oct 2020 05:00:00 -0500

Shreya Shankar is a Machine Learning Engineer at Viaduct AI. She's a master's student at Stanford and has previously worked at Facebook and Google Brain. She writes some truly excellent articles about machine learning on her personal blog, https://www.shreya-shankar.com/

Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletter

Comments? Questions? Submit them here: http://bit.ly/mle-survey

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

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Subscribe to ML Engineered: https://mlengineered.com/listen

Timestamps:

01:30 Follow Charlie on Twitter (http://twitter.com/charlieyouai)

02:40 How Shreya got started in CS

06:00 Choosing to concentrate in systems in undergrad (https://www.shreya-shankar.com/systems/)

12:25 Research at Google Brain on fooling humans with adversarial examples (http://papers.nips.cc/paper/7647-adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans.pdf)

18:00 Deciding to go into industry instead of pursuing a PhD (https://www.shreya-shankar.com/new-grad-advice/)

19:35 Why is putting ML into production so hard? (https://www.shreya-shankar.com/making-ml-work/)

25:00 Best of the research graveyard

29:05 Checklist for building an ML model for production

34:10 Ensuring reproducibility

39:25 Back to the checklist

44:25 PM for ML engineering

48:50 Monitoring ML deployments

53:50 Fighting ML bias

58:45 Feature engineering best practices

01:02:30 Remote collaboration on data science projects

01:07:45 AI Saviorism (https://www.shreya-shankar.com/ai-saviorism/)

01:17:40 Rapid Fire Questions

Links:

Why you should major in systems

Adversarial Examples that Fool Both Computer Vision and Time-Limited Humans

Choosing between a PhD and industry for new computer science graduates

Reflecting on a year of making machine learning actually useful

Get rid of AI Saviorism

Designing Data Intensive Applications

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

Charlie You — Tue, 13 Oct 2020 05:00:00 -0500

Josh Tobin holds a CS PhD from UC Berkeley, which he completed in four years while also working at OpenAI as a research scientist. His focus was on robotic perception and control, and contributed to the famous Rubik's cube robot hand video. He co-organizes the phenomenal Full Stack Deep Learning course and is now working on a new stealth startup.

Learn more about Josh:

http://josh-tobin.com/

https://twitter.com/josh_tobin_

Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: https://mlengineered.ck.page/943aa3fd46

Comments? Questions? Submit them here: https://charlie266.typeform.com/to/DA2j9Md9

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Subscribe to ML Engineered: https://mlengineered.com/listen

Timestamps:

01:32 Follow Charlie on Twitter (twitter.com/charlieyouai)

02:43 How Josh got started in CS and ML

11:05 Why Josh worked on ML for robotics

15:03 ML for Robotics research at OpenAI

28:20 Josh's research process

34:56 Why putting ML into production is so difficult

44:46 What Josh thinks the ML Ops landscape will look like

49:49 Common mistakes that production ML teams and companies make

53:11 How ML systems will be built in the future

59:37 The most valuable skills that ML engineers should develop

01:03:50 Rapid Fire Questions

Links

Full Stack Deep Learning

Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

Domain Randomization and Generative Models for Robotic Grasping

DeepMind Generative Query Network (GQN) paper

Geometry Aware Neural Rendering

Josh's PhD Thesis

OpenAI Rubik's Cube Robot Hand video

Weights and Biases interview with Josh

Building Data Intensive Applications

Creative Selection

Sanyam Bhutani: Chai Time Data Science

Charlie You — Tue, 06 Oct 2020 05:00:00 -0500

Sanyam Bhutani is a Machine Learning Engineer at H2O.ai and host of the Chai Time Data Science Show.

Learn more about Sanyam:

Website: https://sanyambhutani.com/

YouTube: https://www.youtube.com/c/ChaiTimeDataScience

Chai Time Data Science: https://chaitimedatascience.com/

Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: https://mlengineered.ck.page/943aa3fd46

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Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(03:00) How Sanyam got started in CS and ML

(16:20) Getting a CV internship after freshman year

(20:30) Having a vision of using AI to help the masses of India

(23:25) FastAI international fellowship

(27:40) Interviews with Machine Learning Heroes

(36:50) Frustration while training ML models

(43:35) Interviewing Jeremy Howard

(46:00) Creating ML content that resonates

(01:01:00) Working at h2o.ai and making an ML course

(01:11:00) Exciting opportunities in the field now

(01:21:20) Rapid fire questions

(01:26:35) Outro

Links:

Interviews with Machine Learning Heroes

Sanyam's Interview with Jeremy Howard

FastAI

Andrew Ng ML Course

High Performance Python

The Subtle Art of Not Giving a Fuck

H2O.ai Online Courses

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

Charlie You — Tue, 29 Sep 2020 05:00:00 -0500

Devon Bernard is an incredible full-stack engineer, manager, and entrepreneur. He's co-founded multiple companies including FlowActive, Jowl, and Rollio in addition to holding top engineering roles at Enlitic, Axgen, and now Somml.

Learn more about Devon: https://www.linkedin.com/in/devonbernard/

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Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(00:00) Intro

(02:00) How he got started in CS

(03:03) Working for GOOG and MSFT while running 2 startups

(05:10) Learning to program

(10:25) How he got started in entrepreneurship

(17:30) Building an animal crossing trading exchange

(21:37) Designing scalable and maintainable backends

(25:55) Does he use formal design methods (DDD)?

(28:24) What makes for a great engineer?

(36:43) Functional programming

(39:43) Increasing productivity of engineering teams

(45:56) Managing up as an individual contributor

(49:59) Consulting advice

(01:04:20) Health-tech startups

(01:19:27) Exciting opportunities outside of healthcare

(01:28:34) Rapid Fire Questions

Links:

Never Split the Difference

Radical Candor

Catherine Yeo: Fairness in AI and Algorithms

Charlie You — Tue, 22 Sep 2020 20:00:00 -0500

Catherine Yeo is a Harvard undergrad studying Computer Science. She's previously worked for Apple, IBM, and MIT CSAIL in AI research and engineering roles. She writes about machine learning in Towards Data Science and in her new publication Fair Bytes.

Learn more about Catherine: http://catherineyeo.tech/

Read Fair Bytes: http://fairbytes.org/

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Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

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Timestamps:

(02:48) How she was first exposed to CS and ML

(07:06) Teaching a high school class on AI fairness

(10:12) Definition of AI fairness

(16:14) Adverse outcomes if AI bias is never addressed

(22:50) How do "de-biasing" algorithms work?

(27:42) Bias in Natural Language Generation

(36:46) State of AI fairness research

(38:22) Interventions needed?

(43:18) What can individuals do to reduce model bias?

(45:28) Publishing Fair Bytes

(52:42) Rapid Fire Questions

Links:

Defining and Evaluating Fair Natural Language Generation

Man is to Computer Programmer as Woman is to Homemaker?

Gender Shades

GPT-3 Paper: Language Models are Few Shot Learners

How Biased is GPT-3?

Reading List for Fairness in AI Topics

Machine Learning’s Obsession with Kids’ TV Show Characters

Charles Yang: Machine Learning for Scientific Research

Charlie You — Tue, 15 Sep 2020 05:00:00 -0500

Charles Yang is an EECS masters student at UC Berkeley focusing on AI and dynamical systems. He writes the excellent Machine Learning For Science newsletter where he showcases a wide range of use cases for machine learning in scientific research and engineering. Learn more about Charles:

Website: https://charlesxjyang.github.io/

Google Scholar: https://scholar.google.com/citations?user=BYOREdwAAAAJ&hl=en

ML4Sci Newsletter (Highly Recommended!): https://ml4sci.substack.com/

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Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

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Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(02:08) Getting started in material science and machine learning

(08:58) "ImageNet moment" for ML in science

(13:20) Model explainability and transparency

(17:06) Charles' Current Research

(18:40) Embedding existing knowledge into ML models

(22:26) "Bilingual Scientists"

(24:46) Learning ML as a traditional scientist

(28:22) Private vs Public ML Research

(32:42) Rise of open-access research

(35:22) "SOTA chasing" in ML research

(38:10) Scientific ML research processes

(44:34) Applying ML knowledge to a scientific problem

(48:00) Biggest opportunities for ML in science

(51:18) Diversity in the research community

(54:24) Writing the ML4Sci newsletter

(56:20) Keeping up with new research

(01:05:30) Rapid Fire Questions

Links:

Charles' ML4Sci newsletter

Charles' article on AI-powered Science as a Service

Charles' article on Deep Learning in Science

Charles' article on Scientific Gatekeeping

Charles' article on Open Access Research

Google Weather Forecasting paper

Google 2nd Weather Forecasting paper

DeepMind Protein Folding paper

SalesForce Protein Folding paper

ML speeding up simulations by 9+ orders of magnitude (!)

Oak Ridge AI for Science Report

Nature paper using word2vec on MatSci papers

Paper using Graph NNs to find dark matter concentrations

Robert Caro - The Power Broker

Conor Dougherty - Golden Gates

swyx (Shawn Wang): Coding Career Strategy

Charlie You — Tue, 08 Sep 2020 09:00:00 -0500

Blog: https://swyx.io/

Book (Use code MLE30 for 30% off!): https://www.learninpublic.org/

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Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Subscribe to ML Engineered: https://www.mlengineered.com/listen

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(05:30) How was the learning in public idea developed?

(07:45) No zero days

(10:00) Does ego prevent developers from learning in public?

(12:30) Pick up what they put down

(17:30) Strategic thinking about coding careers

(19:50) Betting on new technologies

(24:00) Enhancing existing skills vs learning new things

(27:40) Reading technical books cover-to-cover

(30:00) Systems thinking

(32:00) Updating a digitally-native book

(35:00) Deciding to work at AWS

(38:00) What won't change in tech?

(41:30) Software business models

(43:00) Rapid fire questions

Links:

Free Code Camp interview: Leaving a $350K/year job to learn coding

No Zero Days

You Can Learn A Lot For The Low Price Of Your Ego

Shawn’s book: The Coding Career Handbook

Learn in Public

Marketing Yourself as a Developer

Crossing the Chasm

How to Create Luck

Laws of UX

Eugene Wei - Invisible Asymptotes

Eugene Wei - Status as a Service

Solocast: Learning Machine Learning

Charlie You — Tue, 01 Sep 2020 09:02:00 -0500

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Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(03:00) How did I get exposed to computer science and what made me pursue it?

(10:00) Why machine learning?

(15:10) How did I learn ML? How would I recommend someone do it today?

(27:00) Why start this podcast? What is the goal?

(29:40) Rapid-fire questions

Links:

Marc Andreeson: Software is eating the world

Breaking Smart

Andrew Ng's ML course

Deep Learning textbook

FastAI Practical Deep Learning for Coders

Naval Podcast

swyx: Learn in Public

Seth Godin Akimbo Podcast Workshop

Marcus Aurelius: Meditations

The ONE Thing

Karthik Suresh: Advice for Computer Science Students

Charlie You — Tue, 01 Sep 2020 09:01:00 -0500

Karthik Suresh works as a software engineer at Blend, the leading digital lending platform. He previously worked at Coursera, Biomedtrics, and KloudData. Learn more about Karthik: http://karthiksuresh.me/

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Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

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Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(02:40) How were you exposed to CS? Why did you decide to pursue it?

(10:30) What advice would you give yourself going into college?

(20:00) Does GPA matter?

(30:20) Job hunting in college

(36:05) Working at Blend

(39:00) Internships

(45:50) Startups vs big tech companies

(58:30) Trends in fin-tech

(01:05:00) Rapid fire questions

Links mentioned:

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

Charlie You — Tue, 01 Sep 2020 09:00:00 -0500

Jordan Dunne works as a Technical Program Manager at Google Payments. He previously worked as a Program Manager at Microsoft, Lead Forward-Deployed Engineer at Enlitic, and Product Manager at Vim. Learn more about Jordan: https://www.linkedin.com/in/jordanwdunne/

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Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(02:00) How were you exposed to CS and why did you pursue it?

(03:25) Is software engineering actually engineering?

(06:40) How do you define product management?

(11:05) When did you realize you wanted to be a PM instead of a developer?

(16:40) Project vs Program vs Product Management

(18:35) Effective PM as leverage on a dev team

(24:05) What can engineers do to make PM's lives easier?