Don't Listen Unless You Are Going to Do ML in Production // Kyle Morris // MLOps Coffee Sessions #87

MLOps.community · Advanced ·📰 AI News & Updates ·4y ago
MLOps Coffee Sessions #87 with Kyle Morris, Don't Listen Unless You Are Going to Do ML in Production. // Abstract Companies wanting to leverage ML specializes in model quality (architecture, training method, dataset), but face the same set of undifferentiated work they need to productionize the model. They must find machines to deploy their model on, set it up behind an API, make the inferences fast, cheap, reliable by optimizing hardware, load-balancing, autoscaling, clustering launches per region, queueing long-running tasks... standardizing docs, billing, logging, CI/CD that integrates testing, and more. Banana.dev's aim is to simplify this process for all. This talk outlines our learnings and the trials and tribulations of ML hosting. // Bio Hey all! Kyle did self-driving AI @ Cruise, robotics @ CMU, currently in business @ Harvard. Now he's building banana.dev to accelerate ML! Kyle cares about safely building superhuman AI. Our generation has the chance to build tools that advance society 100x more in our lifetime than in all of history, but it needs to benefit all living things! This requires a lot of technical + social work. Let's go! // MLOps Jobs board https://mlops.pallet.xyz/jobs // Related Links kyle.af --------------- ✌️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, blogs, newsletter and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/ Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kylejohnmorris/ Timestamps: [00:00] Introduction to Kyle Morris [02:42] banana.dev [04:43] banana.dev's vision [06:22] banana.dev's goal beyond the competition [07:28] Computer vision optimization [08:46] Common pitfalls [11:47] Machine Learning Engineering vs Soft
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 13 MLOps and Monitoring
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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
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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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 44 More difficult transition for data scientists to become ML engineers
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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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 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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