Machine Learning Engineered

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...

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
Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/newsletter
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI
Subscribe to ML Engineered: https://mlengineered.com/listen
Comments? Questions? Submit them here: http://bit.ly/mle-survey
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:
02:15 How Radek got interested in programming and computer science
09:00 How Radek taught himself machine learning
26:40 The skills Radek learned from Fast AI
39:20 Radek's recommendations for people learning ML now
51:30 Why Radek is writing a book
01:01:20 Radek's work at the Earth Species Project
01:10:15 How the ESP collects animal language data
01:21:05 Rapid fire questions

Links:
Radek's Book "Meta-Learning"
Andrew Ng ML Coursera
Fast AI
Universal Language Model Fine-tuning for Text Classification
How to do Machine Learning Efficiently
NPR - Two Heartbeats a Minute
Earth Species Project
A Guide to the Good Life
The Origin of Wealth
Make Time
You Are Here

Rodrigo Rivera is a machine learning researcher at the Advanced Data Analytics in Science and Engineering Group at Skoltech and technical director of Samsung Next. He's previously been in data science and research leadership roles at companies all around the world including Rocket Internet and Philip-Morris.
Learn more about Rodrigo:
https://rodrigo-rivera.com/
https://twitter.com/rodrigorivr
Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletter
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI
Subscribe to ML Engineered: https://mlengineered.com/listen
Comments? Questions? Submit them here: http://bit.ly/mle-survey
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:
03:00 How Rodrigo got started in computer science and started his first company
10:40 Rodrigo's experiences leading data science teams at Rocket Internet and PMI
26:15 Leaving industry to get a PhD in machine learning
28:55 Data science collaboration between business and academia
32:45 Rodrigo's research interest in time series data
39:25 Topological data analysis
45:35 Framing effective research as a startup
48:15 Neural Prophet
01:04:10 The potential future of Julia for numerical computing
01:08:20 Most exciting opportunities for ML in industry
01:15:05 Rodrigo's advice for listeners
01:17:00 Rapid fire questions

Links:
Rodrigo's Google Scholar
Advanced Data Analytics in Science and Engineering Group
Neural Prophet
M-Competitions
Machine Learning Refined
Foundations of Machine Learning
A First Course in Machine Learning

Dan Jeffries is the chief technical evangelist at Pachyderm, a leading data science platform. He's a prominent writer and speaker on all things related to the future. He's been in software for over two decades, many of those at Redhat, and is the founder of the AI Infrastructure Alliance and Practical AI Ethics.
Learn more about Dan:
https://twitter.com/Dan_Jeffries1
https://medium.com/@dan.jeffries
Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/newsletter

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI
Subscribe to ML Engineered: https://mlengineered.com/listen
Comments? Questions? Submit them here: http://bit.ly/mle-survey
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

Willem Pienaar is the co-creator of Feast, the leading open source feature store, which he leads the development of as a tech lead at Tecton. Previously, he led the ML platform team at Gojek, a super-app in Southeast Asia.
Learn more:
https://twitter.com/willpienaar
https://feast.dev/
Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletter

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI
Subscribe to ML Engineered: https://mlengineered.com/listen
Comments? Questions? Submit them here: http://bit.ly/mle-survey
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:
02:15 How Willem got started in computer science
03:40 Paying for college by starting an ISP
05:25 Willem's experience creating Gojek's ML platform
21:45 Issues faced that led to the creation of Feast
26:45 Lessons learned building Feast
33:45 Integrating Feast with data quality monitoring tools
40:10 What it looks like for a team to adopt Feast
44:20 Feast's current integrations and future roadmap
46:05 How a data scientist would use Feast when creating a model
49:40 How the feature store pattern handles DAGs of models
52:00 Priorities for a startup's data infrastructure
55:00 Integrating with Amundsen, Lyft's data catalog
57:15 The evolution of data and MLOps tool standards for interoperability
01:01:35 Other tools in the modern data stack
01:04:30 The interplay between open and closed source offerings

Links:
Feast's Github
Gojek Data Science Blog
Data Build Tool (DBT)
Tensorflow Data Validation (TFDV)
A State of Feast
Google BigQuery
Lyft Amundsen
Cortex
Kubeflow
MLFlow