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.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...

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.comhttps://twitter.com/radekosmulskiEvery 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/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 How Radek got interested in programming and computer science09:00 How Radek taught himself machine learning26:40 The skills Radek learned from Fast AI39:20 Radek's recommendations for people learning ML now51:30 Why Radek is writing a book01:01:20 Radek's work at the Earth Species Project01:10:15 How the ESP collects animal language data01:21:05 Rapid fire questionsLinks:Radek's Book "Meta-Learning"Andrew Ng ML CourseraFast AIUniversal Language Model Fine-tuning for Text ClassificationHow to do Machine Learning EfficientlyNPR - Two Heartbeats a MinuteEarth Species ProjectA Guide to the Good LifeThe Origin of WealthMake TimeYou 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/rodrigorivrEvery 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: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:Rodrigo's Google ScholarAdvanced Data Analytics in Science and Engineering GroupNeural ProphetM-CompetitionsMachine Learning RefinedFoundations of Machine LearningA 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_Jeffries1https://medium.com/@dan.jeffriesEvery 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/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 How Dan got started in computer science06:50 What Dan is most excited about in AI14:45 Where we are in the adoption curve of ML20:40 The "Canonical Stack" of ML32:00 Dan's goal for the AI Infrastructure Alliance40:55 "Problems that ML startups don't know they're going to have"49:00 Closed vs open source tools in the Canonical Stack01:00:05 Building out the "boring" part of the infrastructure to enable exciting applications01:08:40 Dan's practical approach to AI Ethics01:23:50 Rapid fire questionsLinks:PachydermAI Infrastructure AlliancePractical AI Ethics AllianceRise of the Canonical Stack in Machine LearningRise of AI - The Age of AI in 2030Google MagentaAlphaGo DocumentaryThinking in BetsA History of the World in 6 GlassesSuper-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/willpienaarhttps://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/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 How Willem got started in computer science03:40 Paying for college by starting an ISP05:25 Willem's experience creating Gojek's ML platform21:45 Issues faced that led to the creation of Feast26:45 Lessons learned building Feast33:45 Integrating Feast with data quality monitoring tools40:10 What it looks like for a team to adopt Feast44:20 Feast's current integrations and future roadmap46:05 How a data scientist would use Feast when creating a model49:40 How the feature store pattern handles DAGs of models52:00 Priorities for a startup's data infrastructure55:00 Integrating with Amundsen, Lyft's data catalog57:15 The evolution of data and MLOps tool standards for interoperability01:01:35 Other tools in the modern data stack01:04:30 The interplay between open and closed source offeringsLinks:Feast's GithubGojek Data Science BlogData Build Tool (DBT)Tensorflow Data Validation (TFDV)A State of FeastGoogle BigQueryLyft AmundsenCortexKubeflowMLFlow

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/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/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 Benedikt Koller05:30 What the "DevOps revolution" was10:10 Bringing good Ops practices into ML projects30:50 Pivoting from vehicle predictive analytics to open source ML tooling34:35 Design decisions made in ZenML39:20 Most common problems faced by applied ML teams49:00 The importance of separating configurations from code55:25 Resources Ben recommends for learning Ops57:30 What to monitor in an ML pipelines01:00:45 Why you should run experiments in automated pipelines01:08:20 The essential components of an MLOps stack01:10:25 Building an open source business and what's next for ZenML01:20:20 Rapid fire questionsLinks:ZenML's GitHubMaiot BlogThe Twelve Factor App12 Factors of reproducible Machine Learning in productionSeldonPachydermKubeFlowSomething Deeply HiddenThe Expanse SeriesThe Three Body ProblemExtreme 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://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/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 Josh Albrecht03:30 How Josh got started in computer science06:35 Josh's first two startup attempts09:15 The tech behind Sourceress, an AI recruiting platform16:10 Pivoting from Sourceress to Generally Intelligent, an AI research lab23:50 How Josh defines "general intelligence"28:35 Why Josh thinks self-supervised learning is the current most promising research area36:15 Generally Intelligent's immediate research roadmap: BYOL, simulated environments59:20 How Josh thinks about creating an optimal research environment01:11:35 The "why" behind starting an independent research lab01:13:30 AI alignment01:17:00 Rapid fire questionsLinks:Bootstrap your own latent: A new approach to self-supervised LearningUnderstanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)BYOL works even without batch statisticsGenerally Intelligent PodcastConsequences of Misaligned AIWhy We SleepPeak

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/elenasamuylovahttps://twitter.com/EmeliDralEvery 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/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 How Emeli and Elena each got started in data science07:10 Applying machine learning across a wide variety of industries at the Yandex Data Factory14:55 Using ML for industrial process improvement23:35 Challenges encountered in industrial ML and technical solutions27:15 The huge opportunity for ML in manufacturing34:35 How to ensure safety when using models in physical systems37:40 Why they started working on tools for data and ML monitoring42:50 Different kinds of data drift and how to address them48:25 Common mistakes ML teams make in monitoring55:25 Features of Evidently AI's library57:35 Building open source software01:02:25 Technical roadmap for Evidently01:05:50 Monitoring complex data01:08:50 Business roadmap for Evidently01:11:35 Rapid fire questionsLinks:Evidently on GithubEvidently AI's BlogThinking Fast and SlowFlowDoing 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/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:02:45 How Hari got started in computer science and machine learning06:00 Making the transition from IC to manager14:35 What it means to be an effective engineering manager19:20 Differences in managing machine learning vs traditional software teams24:30 The importance of explaining complicated topics simply30:15 How he thinks about hiring for data science and machine learning36:50 Mistakes Workday made as it adopted machine learning41:50 Essential skills for machine learning engineers54:05 Why the future of AI is augmentation, not automation58:30 His experience so far with YC01:02:00 Rapid fire questionsLinks:Growth MindsetThe Feynman TechniqueRadical CandorTrillion Dollar CoachMultipliersGood to GreatThe First 90 DaysCrossing the ChasmZero to OneThe Lean StartupThe Hard Thing About Hard ThingsSapiensA Short History of Nearly EverythingOn IntelligencePrediction MachinesAlgorithms to Live By<a...

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/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:02:45 Intro04:10 How I got into data science and machine learning08:25 My experience working as an ML engineer and starting the podcast12:15 Project management methods for machine learning20:50 ML job roles are trending towards more specialization26:15 ML tools enable collaboration between roles and encode best practices34:00 Data privacy, security, and provenance as first class considerations39:30 The future of managed ML platforms and cloud providers49:05 What I've learned about building a career in ML engineering54:10 Dealing with information overloadLinks:Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in ProductionThe Third Wave Data ScientistPractical ML Ops // Noah Gift // MLOps Coffee SessionsBuilding a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)SRE for ML Infra // Todd Underwood // MLOps Coffee SessionsLuigi Patruno on the ML Ops Community podcastLuigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"