Machine Learning Engineered

By: Charlie You
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  • Summary

  • This podcast helps Machine Learning Engineers become the best at what they do. Join host Charlie You every week as he talks to the brightest minds in data science, artificial intelligence, and software engineering to discover how they bring cutting edge research out of the lab and into products that people love. You'll learn the skills, tools, and best practices you can use to build better ML systems and accelerate your career in this flourishing new field.
    © 2020 You Enterprises LLC. All Rights Reserved.
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Episodes
  • Diving Deep into Synthetic Data with Alex Watson of Gretel.ai

    Diving Deep into Synthetic Data with Alex Watson of Gretel.ai
    Apr 20 2021
    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.aiEvery 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/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 Introducing Alex Watson03:45 How Alex was first exposed to programming05:00 Alex's experience starting Harvest AI, getting acquired by AWS, and integrating their product at massive scale21:20 How Alex first saw the opportunity for Gretel.ai24:20 The most exciting use-cases for synthetic data28:55 Theoretical guarantees of anonymized data with differential privacy36:40 Combining pre-training with synthetic data38:40 When to anonymize data and when to synthesize it41:25 How Gretel's synthetic data engine works44:50 Requirements of a dataset to create a synthetic version49:25 Augmenting datasets with synthetic examples to address representation bias52:45 How Alex recommends teams get started with Gretel.ai59:00 Expected accuracy loss from training models on synthetic data01:03:15 Biggest surprises from building Gretel.ai01:05:25 Organizational patterns for protecting sensitive data01:07:40 Alex's vision for Gretel's data catalog01:11:15 Rapid fire questionsLinks:Gretel.ai BlogNetFlix Cancels Recommendation Contest After Privacy LawsuitGreylock - The Github of DataImproving massively imbalanced datasets in machine learning with synthetic dataDeep dive on generating synthetic data for HealthcareGretel’s New Synthetic Performance ReportThe...
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    1 hr and 19 mins
  • A Practical Approach to Learning Machine Learning with Radek Osmulski (Earth Species Project)

    A Practical Approach to Learning Machine Learning with Radek Osmulski (Earth Species Project)
    Mar 30 2021

    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
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    1 hr and 38 mins
  • From Data Science Leader to ML Researcher with Rodrigo Rivera (Skoltech ADASE, Samsung NEXT)

    From Data Science Leader to ML Researcher with Rodrigo Rivera (Skoltech ADASE, Samsung NEXT)
    Mar 23 2021

    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
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    1 hr and 24 mins
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