Episodi
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Alex Watson is the co-founder and CEO of http://Gretel.ai (Gretel.ai), a startup that offers APIs for creating anonymized and synthetic datasets. Previously he was the founder of http://Harvest.ai (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 (http://gretel.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 (https://www.cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
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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 http://Gretel.ai (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 http://Gretel.ai (Gretel.ai)
59:00 Expected accuracy loss from training models on synthetic data
01:03:15 Biggest surprises from building http://Gretel.ai (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:
https://gretel.ai/blog (Gretel.ai Blog)
https://www.wired.com/2010/03/netflix-cancels-contest/ (NetFlix Cancels Recommendation Contest After Privacy Lawsuit)
https://greylock.com/portfolio-news/the-github-of-data/ (Greylock - The Github of Data)
https://gretel.ai/blog/improving-massively-imbalanced-datasets-in-machine-learning-with-synthetic-data (Improving massively imbalanced datasets in machine learning with synthetic data)
https://gretel.ai/blog/deep-dive-on-generating-synthetic-data-for-healthcare (Deep dive on generating synthetic data for Healthcare)
https://medium.com/gretel-ai/synthetic-data-performance-report-e5a3cd6b1e6d (Gretel’s New Synthetic Performance Report)
https://www.goodreads.com/book/show/18007564-the-martian (The Martian)
https://www.penguinrandomhouse.com/books/172832/snow-crash-by-neal-stephenson/ (Snow Crash)
https://us.macmillan.com/series/themurderbotdiaries/ (The MurderBot Diaries) -
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://www.radekosmulski.com)
https://twitter.com/radekosmulski (https://twitter.com/radekosmulski)
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 (http://cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
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Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
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:
https://gumroad.com/l/learn_deep_learning (Radek's Book "Meta-Learning")
https://www.coursera.org/learn/machine-learning (Andrew Ng ML Coursera)
https://www.fast.ai (Fast AI)
https://arxiv.org/abs/1801.06146 (Universal Language Model Fine-tuning for Text Classification)
https://www.kdnuggets.com/2018/03/machine-learning-efficiently.html (How to do Machine Learning Efficiently)
https://www.npr.org/2020/02/25/809336135/two-heartbeats-a-minute (NPR - Two Heartbeats a Minute)
https://www.earthspecies.org/ (Earth Species Project)
https://www.goodreads.com/book/show/5617966-a-guide-to-the-good-life (A Guide to the Good Life)
https://store.hbr.org/product/the-origin-of-wealth-evolution-complexity-and-the-radical-remaking-of-economics/777X (The Origin of Wealth)
https://maketime.blog (Make Time)
https://plumvillage.org/books/you-are-here/ (You Are Here) -
Episodi mancanti?
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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://rodrigo-rivera.com/)https://twitter.com/rodrigorivr (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 (https://www.cyou.ai/newsletter)Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)Timestamps:03:00 How Rodrigo got started in computer science and started his first company10:40 Rodrigo's experiences leading data science teams at Rocket Internet and PMI26:15 Leaving industry to get a PhD in machine learning28:55 Data science collaboration between business and academia32:45 Rodrigo's research interest in time series data39:25 Topological data analysis45:35 Framing effective research as a startup48:15 Neural Prophet01:04:10 The potential future of Julia for numerical computing01:08:20 Most exciting opportunities for ML in industry01:15:05 Rodrigo's advice for listeners01:17:00 Rapid fire questionsLinks:https://scholar.google.de/citations?user=nQGmpjUAAAAJ&hl=en (Rodrigo's Google Scholar)http://adase.group (Advanced Data Analytics in Science and Engineering Group)http://neuralprophet.com (Neural Prophet)https://en.wikipedia.org/wiki/Makridakis_Competitions (M-Competitions)https://www.cambridge.org/us/academic/subjects/engineering/communications-and-signal-processing/machine-learning-refined-foundations-algorithms-and-applications-2nd-edition?format=HB (Machine Learning Refined)https://cs.nyu.edu/~mohri/mlbook/ (Foundations of Machine Learning)http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ (A First Course in Machine Learning)
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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://twitter.com/Dan_Jeffries1)
https://medium.com/@dan.jeffries (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 (http://cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (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:
https://www.pachyderm.com/ (Pachyderm)
https://ai-infrastructure.org/ (AI Infrastructure Alliance)
https://practical-ai-ethics.org/ (Practical AI Ethics Alliance)
https://towardsdatascience.com/rise-of-the-canonical-stack-in-machine-learning-724e7d2faa75 (Rise of the Canonical Stack in Machine Learning)
https://www.youtube.com/watch?v=q_KPNtmc9m8 (Rise of AI - The Age of AI in 2030)
https://magenta.tensorflow.org/ (Google Magenta)
https://www.youtube.com/watch?v=WXuK6gekU1Y (AlphaGo Documentary)
https://www.annieduke.com/books/ (Thinking in Bets)
https://www.goodreads.com/book/show/3872.A_History_of_the_World_in_6_Glasses (A History of the World in 6 Glasses)
https://www.penguinrandomhouse.com/books/562923/super-thinking-by-gabriel-weinberg-and-lauren-mccann/ (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://twitter.com/willpienaar)
https://feast.dev/ (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 (https://www.cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (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:
https://github.com/feast-dev/feast (Feast's Github)
https://blog.gojekengineering.com/data-science/home (Gojek Data Science Blog)
https://www.getdbt.com/ (Data Build Tool (DBT))
https://www.tensorflow.org/tfx/data_validation/get_started (Tensorflow Data Validation (TFDV))
https://feast.dev/post/a-state-of-feast/ (A State of Feast)
https://cloud.google.com/bigquery (Google BigQuery)
https://www.amundsen.io/ (Lyft Amundsen)
https://www.cortex.dev/ (Cortex)
https://www.kubeflow.org/ (Kubeflow)
https://mlflow.org/ (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://zenml.io/)
https://maiot.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 (https://www.cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (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:
https://github.com/maiot-io/zenml (ZenML's GitHub)
https://blog.maiot.io/ (Maiot Blog)
https://12factor.net/ (The Twelve Factor App)
https://blog.maiot.io/12-factors-of-ml-in-production/ (12 Factors of reproducible Machine Learning in production)
https://www.seldon.io/ (Seldon)
https://www.pachyderm.com/ (Pachyderm)
https://www.kubeflow.org/ (KubeFlow)
https://www.penguinrandomhouse.com/books/566988/something-deeply-hidden-by-sean-carroll/ (Something Deeply Hidden)
https://www.goodreads.com/series/56399-the-expanse (The Expanse Series)
https://us.macmillan.com/books/9780765382030 (The Three Body Problem)
https://echelonfront.com/extreme-ownership/ (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://joshalbrecht.com/)
http://generallyintelligent.ai/ (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 (https://www.cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (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:
https://arxiv.org/abs/2006.07733 (Bootstrap your own latent: A new approach to self-supervised Learning)
https://generallyintelligent.ai/understanding-self-supervised-contrastive-learning.html (Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL))
https://arxiv.org/abs/2010.10241 (BYOL works even without batch statistics)
https://open.spotify.com/show/1hikWa5LWDQJwXtz5LoeVn (Generally Intelligent Podcast)
https://arxiv.org/abs/2102.03896 (Consequences of Misaligned AI)
https://www.goodreads.com/book/show/34466963-why-we-sleep (Why We Sleep)
https://www.goodreads.com/book/show/26312997-peak (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://evidentlyai.com/)
https://twitter.com/elenasamuylova (https://twitter.com/elenasamuylova)
https://twitter.com/EmeliDral (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 (http://cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.notion.so/charlieyou/Content-Pipeline-af923f8b990646369a85a00a348a1e12 (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:
https://github.com/evidentlyai/evidently (Evidently on Github)
https://evidentlyai.com/blog (Evidently AI's Blog)
https://us.macmillan.com/books/9780374533557 (Thinking Fast and Slow)
https://www.goodreads.com/book/show/66354.Flow (Flow)
https://www.effectivealtruism.org/doing-good-better/ (Doing Good Better) -
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 (https://cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.notion.so/charlieyou/Content-Pipeline-af923f8b990646369a85a00a348a1e12 (https://www.givingwhatwecan.org/)
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:
https://www.brainpickings.org/2014/01/29/carol-dweck-mindset/ (Growth Mindset)
https://fs.blog/2012/04/feynman-technique/ (The Feynman Technique)
https://www.radicalcandor.com/ (Radical Candor)
https://www.trilliondollarcoach.com/ (Trillion Dollar Coach)
https://thewisemangroup.com/books/multipliers/ (Multipliers)
https://www.jimcollins.com/article_topics/articles/good-to-great.html#articletop (Good to Great)
https://hbr.org/books/watkins (The First 90 Days)
https://www.harpercollins.com/products/crossing-the-chasm-3rd-edition-geoffrey-a-moore?variant=32130444066850 (Crossing the Chasm)
https://en.wikipedia.org/wiki/Zero_to_One (Zero to One)
http://theleanstartup.com/ (The Lean Startup)
https://hardthings.bhorowitz.com/ (The Hard Thing About Hard Things)
https://www.ynharari.com/book/sapiens-2/ (Sapiens)
https://www.penguinrandomhouse.com/books/20549/a-short-history-of-nearly-everything-special-illustrated-edition-by-bill-bryson/ (A Short History of Nearly Everything)
https://numenta.com/resources/on-intelligence/ (On Intelligence)
https://www.predictionmachines.ai/ (Prediction Machines)
https://algorithmstoliveby.com/ (Algorithms to Live By)
https://sdv.dev/ (The Synthetic Data Vault) -
Learn more about the ML Ops Community: https://mlops.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 (https://cyou.ai/newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
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:
https://www.mlengineered.com/episode/josh-tobin (Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production)
https://towardsdatascience.com/the-third-wave-data-scientist-1421df7433c9 (The Third Wave Data Scientist)
https://www.youtube.com/watch?v=GvAyV8m8ICI (Practical ML Ops // Noah Gift // MLOps Coffee Sessions)
https://cyou.ai/podcast/pavle-jeremic/ (Building a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio))
https://www.youtube.com/watch?v=Fu87cHHfOE4 (SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions)
https://www.youtube.com/watch?v=ShBod1yXUeg (Luigi Patruno on the ML Ops Community podcast)
https://www.mlengineered.com/episode/luigi-patruno (Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0") -
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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
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:
https://podcasts.apple.com/us/podcast/4-making-alchemy-real-pavle-jeremic-aether-biomachines/id1498805236?i=1000465399648 (Founders First Interview - Making Alchemy Real)
https://deepchem.io/ (DeepChem)
https://en.wikipedia.org/wiki/Engines_of_Creation (Engines of Creation)
https://www.goodreads.com/series/49121-rama (Rama Series) -
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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
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Timestamps:
02:50 https://www.mlengineered.com/episode/josh-tobin (Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production)
21:48 https://www.mlengineered.com/episode/shreya-shankar (Shreya Shankar: Lessons learned after a year of putting ML into production)
34:44 https://www.mlengineered.com/episode/luigi-patruno (Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0")
53:28 https://www.mlengineered.com/episode/andreas-jansson (Music Information Retrieval at Spotify and the Future of ML Tooling with Andreas Jansson of Replicate) -
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 (http://bitly.com/mle-newsletter)
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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://replicate.ai/)
https://www.linkedin.com/in/janssonandreas/ (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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
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/ (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:
https://deepmind.com/blog/article/wavenet-generative-model-raw-audio (WaveNet: a generative model for raw audio)
https://openaccess.city.ac.uk/id/eprint/19289/1/ (Singing Voice Separation with Deep U-Net CNNs)
https://openaccess.city.ac.uk/id/eprint/23669/1/ (Joint Singing Voice Separation and F0 Estimation with Deep U-Net Architectures)
https://www.arxiv-vanity.com/ (arXiv Vanity)
https://replicate.ai/ (Replicate)
https://discord.gg/QmzJApGjyE (Replicate's Discord) -
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://mlinproduction.com/)
https://twitter.com/mlinproduction (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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
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:
https://datacast.simplecast.com/episodes/luigi-patruno (Luigi's interview on Datacast)
https://mlinproduction.com/deploying-machine-learning-models/ (Ultimate Guide to Deploying ML Models)
https://mlinproduction.com/maximizing-business-impact-with-machine-learning/ (Maximizing Business Impact with Machine Learning)
https://mlinproduction.com/newsletter-083/ (Two Types of Companies Using ML)
https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007 (The AI Hierarchy of Needs)
https://www.mlengineered.com/episode/josh-tobin (Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production)
https://mlinproduction.com/machine-learning-is-forcing-software-development-to-evolve/ (Machine Learning is Forcing Software Development to Evolve)
https://www.youtube.com/watch?v=fTvB5xMNfTY (ML Street Talk #29: GPT-3, Prompt Engineering, Trading, AI Alignment, Intelligence)
https://www.oreilly.com/library/view/building-machine-learning/9781492045106/ (Building Machine Learning Powered Applications)
https://www.penguinrandomhouse.com/books/529343/how-to-change-your-mind-by-michael-pollan/ (How to Change Your Mind)
https://stevenpressfield.com/books/the-war-of-art/ (The War of Art)
https://www.ynharari.com/book/sapiens-2/ (Sapiens) -
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 https://www.learninpublic.org/ (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://swyx.io/)
https://www.learninpublic.org/ (https://www.learninpublic.org/)
https://twitter.com/swyx (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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (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/ (https://careerchats.transistor.fm/))
Links:
https://www.mlengineered.com/episode/swyx (swyx's first ML Engineered appearance)
https://www.learninpublic.org/ (swyx's book Coding Career Handbook)
https://www.swyx.io/create_luck/ (How to Create Luck)
https://www.swyx.io/time-management-randy-pausch/ (Notes on Time Management from a Dying Professor)
https://www.buildingasecondbrain.com/ (Building a Second Brain)
https://simplenote.com/ (SimpleNote)
https://careerchats.transistor.fm/ (swyx's new podcast with Randall Kanna "Career Chats") -
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 (https://twitter.com/ykilcher)
Check out Yannic's excellent Youtube channel: https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew (https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew)
Listen to the ML Street Talk podcast: https://podcasts.apple.com/us/podcast/machine-learning-street-talk/id1510472996 (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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
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:
https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew (Yannic's amazing Youtube Channel)
https://www.notion.so/Yannic-Kilcher-e93c81f81100464399e173867815e380 (Yannic's Google Scholar)
https://discord.gg/4H8xxDF (Yannic's Community Discord Channel)
On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 (arXiv paper) and https://www.youtube.com/watch?v=3_qGrmD6iQY (Yannic's video series)
https://www.youtube.com/watch?v=Uumd2zOOz60 (How I Read a Paper: Facebook's DETR (Video Tutorial))
https://www.youtube.com/watch?v=TrdevFK_am4 (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained))
https://fs.blog/2014/09/peter-thiel-zero-to-one/ (Zero to One)
https://www.penguin.co.uk/books/104/1049544/the-gulag-archipelago/9781784871512.html (The Gulag Archipelago) -
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://zakslayback.com/)
https://www.1517fund.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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
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/ (https://zakslayback.com/frameworks-success-opportunity-cost/))
14:35 How to set career goals (https://zakslayback.com/ambition-mapping/ (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/ (https://zakslayback.com/whats-difference-mentors-advisors-coaches/))
35:40 Finding a mentor (https://zakslayback.com/professional-mentor-dream-job/ (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/ (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 (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:
https://www.nateliason.com/podcast/zak-slayback (The End of School and Building a Valuable Skillset with Zak Slayback)
https://schoolsucksproject.com/deschool-yourself-find-your-focus-zak-slayback/ (Deschool Yourself and Find Your Focus – With Zak Slayback)
https://zakslayback.com/book/ (Zak's book - How to Get Ahead (highly recommended!))
https://zakslayback.com/ambition-mapping/ (Ambition Mapping)
https://iep.utm.edu/girard/ (Rene Girard and Mimetic Desire)
https://commoncog.com/blog/tacit-knowledge-is-a-real-thing/ (Why Tacit Knowledge is More Important Than Deliberate Practice)
https://zakslayback.com/professional-mentor-dream-job/ (How to Get Your Dream Job and Mentor in 6 Easy Steps)
https://zakslayback.com/whats-difference-mentors-advisors-coaches/ (What’s The Difference Between Mentors, Advisors, and Coaches?)
https://zakslayback.com/keynote-video-get-ahead-nothing-offer/ (How to Get Ahead When You Have Nothing to Offer)
https://zakslayback.com/why-start-a-website/ (“Why Should I Start a Website?”)
https://zakslayback.com/frameworks-success-opportunity-cost/ (Frameworks for Making Better Decisions: Opportunity Cost)
https://www.amazon.com/How-Fail-Almost-Everything-Still-ebook/dp/B00COOFBA4 (How to Fail at Almost Everything and Still Win Big)
https://medium.com/1517/a-new-95-ec071200d98f (The New 95 Theses)
https://www.penguinrandomhouse.com/books/176227/antifragile-by-nassim-nicholas-taleb/ (Antifragile) -
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.cl.uni-heidelberg.de/~parcalabescu/)
https://www.youtube.com/channel/UCobqgqE4i5Kf7wrxRxhToQA (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 (http://bitly.com/mle-newsletter)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Comments? Questions? Submit them here: http://bitly.com/mle-survey (http://bitly.com/mle-survey)
Timestamps:
01:30 Follow Charlie on Twitter (https://twitter.com/CharlieYouAI (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 (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 (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 (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 (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/ (https://www.youtube.com/c/AICoffeeBreakwithLetitiaParcalabescu/))
01:18:10 Rapid fire questions
Links:
https://www.youtube.com/channel/UCobqgqE4i5Kf7wrxRxhToQA (AI Coffee Break Youtube Channel)
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 (Exploring Phrase Grounding without Training)
https://www.youtube.com/playlist?list=PLpZBeKTZRGPNKxoNaeMD9GViU_aH_HJab (AI Coffee Break series on Multi-Modal learning)
https://www.youtube.com/watch?v=-niprVHNrgI (What does it take for an AI to understand language?)
https://arxiv.org/abs/1908.02265 (ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations) -
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://moinnadeem.com/)
https://twitter.com/moinnadeem (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 (http://bit.ly/mle-newsletter)
Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey)
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI)
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/)
Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen)
Timestamps:
01:35 Follow Charlie on Twitter (https://twitter.com/CharlieYouAI (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/ (https://www.aclweb.org/anthology/N19-4014/))
25:15 Predicting statement stance with neural multi-task learning (https://www.aclweb.org/anthology/D19-6603/ (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 (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:
https://www.aclweb.org/anthology/N19-4014/ (FAKTA: An Automatic End-to-End Fact Checking System)
https://stereoset.mit.edu/ (StereoSet: Measuring stereotypical bias in pretrained language models)
https://www.aclweb.org/anthology/D19-6603/ (Neural Multi-Task Learning for Stance Prediction)
http://www.incompleteideas.net/IncIdeas/BitterLesson.html (Rich Sutton - The Bitter Lesson)
https://arxiv.org/abs/2009.07243 (A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation)
https://arxiv.org/abs/1905.12265 (Strategies for Pre-training Graph Neural Networks)
https://openreview.net/pdf?id=YicbFdNTTy (Transformers For Image Recognition at Scale)
https://www.cerebras.net/product/ (Cerebras CS-1)
https://www.tryklarity.com/ (Klarity: AI for Law Contract Review)
https://www.mit.edu/~jda/ (Jacob Andreas)
https://cs.stanford.edu/people/jure/ (Jure Leskovec)
https://www.simonandschuster.com/books/Shoe-Dog/Phil-Knight/9781501135927 (Shoe Dog)
https://en.m.wikipedia.org/wiki/Alexander_Hamilton_(book) (Hamilton)
https://becomingmichelleobama.com/ (Becoming)
https://www.penguinrandomhouse.com/books/44330/mindset-by-carol-s-dweck-phd/ (Mindset)
https://en.m.wikipedia.org/wiki/The_Innovators_(book) (The Innovators) - Mostra di più
