The Next Revolution in AI: LLMs and Beyond

MLOps.community · Beginner ·🛠️ AI Tools & Apps ·1y ago
//Abstract LLMs have become the de-facto standard in modern AI toolchain, but it still comes with a lot of confusions - quality, speed, cost, etc. In this talk, I will share a few observations we have regarding LLM, both from an algorithm engineer and an infra engineer perspective, on how we should best utilize LLMs in our daily operations. I'll also touch a bit topic on how enterprises think of their IT and AI strategy, given that the fast changing computation pattern is disrupting conventional cloud in unprecedented ways. //Bio Yangqing is the co-founder and CEO of Lepton AI, a startup helping enterprises run AI applications efficiently, at scale, and in minutes. Before this, Yangqing served as VP of Alibaba on Big Data and AI. He led AI infra at Facebook, and before that did research at Google Brain. He created and contributed to open-source libraries like Caffe, ONNX, and PyTorch. Yangqing graduated from UC Berkeley with a PhD in EECS. A big thank you to our Premium Sponsors @Databricks, @tecton8241, & @onehouseHQfor their generous support!
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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 7 Explainability, Black boxes and EU white paper on reproducibility
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 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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?
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
Building an MLOps Team? Key ideas to keep in mind
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 11 Hierarchy of MLOps Needs
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
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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Friction Between Data Scientists and Software Engineers
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ML Platforms - The build vs buy question
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 20 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
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Message buses, Async and sync architecture
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MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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 23 Hybrid Data Science Teams @SurveyMonkey
Hybrid Data Science Teams @SurveyMonkey
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 25 Doing ML with Personal Information
Doing ML with Personal Information
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Evolution of the ML feature store @SurveyMonkey
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 27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
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 28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
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 29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
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MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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 32 MLOps: Airflow Pros and Cons
MLOps: Airflow Pros and Cons
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 33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
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 34 Current State Of Machine Learning
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
Learning from real life Machine Learning failures
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 37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
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 38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
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 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
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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
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
Speed, Trust, Evolution and Scale in MLOps
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 44 More difficult transition for data scientists to become ML engineers
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?
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
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
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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 55 Why Kubeflow gained so much traction=open community
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