Tour of Upcoming Features on the Hugging Face Model Hub// Julien Chaumond //MLOps Coffee Sessions#48

MLOps.community · Beginner ·📐 ML Fundamentals ·4y ago
Coffee Sessions #48 with Julien Chaumond, Tour of Upcoming Features on the Hugging Face Model Hub. //Abstract Julien Chaumond’s Tour of Upcoming Features on the Hugging Face Model Hub. Our MLOps community guest in this episode is Julien Chaumond the CTO of Hugging Face - every data scientist’s favorite NLP Swiss army knife. Julien, David, and Demetrios spoke about many topics including: Infra for hosting models/model hubs Inference widgets for companies with CPUs & GPUs (for companies) Auto NLP which trains models “Infrastructure as a service” // Bio Julien Chaumond is Chief Technical Officer at Hugging Face, a Brooklyn and Paris-based startup working on Machine learning and Natural Language Processing, and is passionate about democratizing state-of-the-art AI/ML for everyone. --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Julien on LinkedIn: https://www.linkedin.com/in/julienchaumond/ Timestamps: [00:00] Introduction to Julien Chaumond [01:57] Julien's background in tech [04:35] "I have this vision of building a community where the greatest people in AI can come together and basically invent the future of Machine Learning together." [04:55] What is Hugging Face? [06:17] "We have the goal of bridging the gap between research and production on actual production use cases." [06:45] Start of open-source in Hugging Face [07:50] Chatbox experiment (reference resolution system) - linking pronouns to the subjects of sentences [10:20] From a project to a company [11:46] "The goal was to explore in the beginning." [11:57] Importance of platform [14
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 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
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 10 Building an MLOps Team? Key ideas to keep in mind
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 11 Hierarchy of MLOps Needs
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 12 Bare necessities for getting an ML model into production
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 13 MLOps and Monitoring
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 14 How Phil Winder got into Data Science and Software Engineering
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 15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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 16 Friction Between Data Scientists and Software Engineers
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 19 ML Platforms - The build vs buy question
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 20 ML Services Gateway at SurveyMonkey
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 25 Doing ML with Personal Information
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 27 Developing a Machine Learning Feature Store
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 28 Auto retrain ML models is not the question
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 29 3 key parts to Machine Learning monitoring
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 30 MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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 32 MLOps: Airflow Pros and Cons
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 33 Specific challenges in Machine Learning
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 34 Current State Of Machine Learning
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 35 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
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 36 Learning from real life Machine Learning failures
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 37 Survivorship Bias in machine learning tutorials
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 38 Swiss Cheese model in Machine Learning
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 39 Resume driven development in Machine learning & software engineering
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 40 Who has the highest standards in ML?
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 41 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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 42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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 43 Speed, Trust, Evolution and Scale in MLOps
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 44 More difficult transition for data scientists to become ML engineers
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 45 How many models in prod til I need a dedicated ML platform?
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 46 Deeper thinking from data scientists around platform blackholes
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 47 Checkpointing, metadata, and confidence in your data
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 48 Adjacent usecases and multistep feature engineering
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 49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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 50 Reproducability flaws in end to end Machine Learning debugging
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 51 3rd wave of data scientists
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 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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