Machine Learning Engineered

Alex Watson is the co-founder and CEO of Gretel.ai [http://Gretel.ai], a startup that offers APIs for creating anonymized and synthetic datasets. Previously he was the founder of Harvest.ai [http://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] 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 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 [http://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 [http://Gretel.ai] 59:00 Expected accuracy loss from training models on synthetic data 01:03:15 Biggest surprises from building Gretel.ai [http://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 [https://gretel.ai/blog] NetFlix Cancels Recommendation Contest After Privacy Lawsuit [https://www.wired.com/2010/03/netflix-cancels-contest/] Greylock - The Github of Data [https://greylock.com/portfolio-news/the-github-of-data/] Improving massively imbalanced datasets in machine learning with synthetic data [https://gretel.ai/blog/improving-massively-imbalanced-datasets-in-machine-learning-with-synthetic-data] Deep dive on generating synthetic data for Healthcare [https://gretel.ai/blog/deep-dive-on-generating-synthetic-data-for-healthcare] Gretel’s New Synthetic Performance Report [https://medium.com/gretel-ai/synthetic-data-performance-report-e5a3cd6b1e6d] The Martian [https://www.goodreads.com/book/show/18007564-the-martian] Snow Crash [https://www.penguinrandomhouse.com/books/172832/snow-crash-by-neal-stephenson/] The MurderBot Diaries [https://us.macmillan.com/series/themurderbotdiaries/]

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] 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 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" [https://gumroad.com/l/learn_deep_learning] Andrew Ng ML Coursera [https://www.coursera.org/learn/machine-learning] Fast AI [https://www.fast.ai] Universal Language Model Fine-tuning for Text Classification [https://arxiv.org/abs/1801.06146] How to do Machine Learning Efficiently [https://www.kdnuggets.com/2018/03/machine-learning-efficiently.html] NPR - Two Heartbeats a Minute [https://www.npr.org/2020/02/25/809336135/two-heartbeats-a-minute] Earth Species Project [https://www.earthspecies.org/] A Guide to the Good Life [https://www.goodreads.com/book/show/5617966-a-guide-to-the-good-life] The Origin of Wealth [https://store.hbr.org/product/the-origin-of-wealth-evolution-complexity-and-the-radical-remaking-of-economics/777X] Make Time [https://maketime.blog] You Are Here [https://plumvillage.org/books/you-are-here/]

Rodrigo Rivera is a machine learning researcher at the Advanced Data Analytics in Science and Engineering Group at Skoltech and technical director of Samsung Next. He's previously been in data science and research leadership roles at companies all around the world including Rocket Internet and Philip-Morris. Learn more about Rodrigo: https://rodrigo-rivera.com/ [https://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 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 [https://scholar.google.de/citations?user=nQGmpjUAAAAJ&hl=en] Advanced Data Analytics in Science and Engineering Group [http://adase.group] Neural Prophet [http://neuralprophet.com] M-Competitions [https://en.wikipedia.org/wiki/Makridakis_Competitions] Machine Learning Refined [https://www.cambridge.org/us/academic/subjects/engineering/communications-and-signal-processing/machine-learning-refined-foundations-algorithms-and-applications-2nd-edition?format=HB] Foundations of Machine Learning [https://cs.nyu.edu/~mohri/mlbook/] A First Course in Machine Learning [http://www.dcs.gla.ac.uk/~srogers/firstcourseml/]

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: Pachyderm [https://www.pachyderm.com/] AI Infrastructure Alliance [https://ai-infrastructure.org/] Practical AI Ethics Alliance [https://practical-ai-ethics.org/] Rise of the Canonical Stack in Machine Learning [https://towardsdatascience.com/rise-of-the-canonical-stack-in-machine-learning-724e7d2faa75] Rise of AI - The Age of AI in 2030 [https://www.youtube.com/watch?v=q_KPNtmc9m8] Google Magenta [https://magenta.tensorflow.org/] AlphaGo Documentary [https://www.youtube.com/watch?v=WXuK6gekU1Y] Thinking in Bets [https://www.annieduke.com/books/] A History of the World in 6 Glasses [https://www.goodreads.com/book/show/3872.A_History_of_the_World_in_6_Glasses] Super-Thinking [https://www.penguinrandomhouse.com/books/562923/super-thinking-by-gabriel-weinberg-and-lauren-mccann/]

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: Feast's Github [https://github.com/feast-dev/feast] Gojek Data Science Blog [https://blog.gojekengineering.com/data-science/home] Data Build Tool (DBT) [https://www.getdbt.com/] Tensorflow Data Validation (TFDV) [https://www.tensorflow.org/tfx/data_validation/get_started] A State of Feast [https://feast.dev/post/a-state-of-feast/] Google BigQuery [https://cloud.google.com/bigquery] Lyft Amundsen [https://www.amundsen.io/] Cortex [https://www.cortex.dev/] Kubeflow [https://www.kubeflow.org/] MLFlow [https://mlflow.org/]