What is AI Quality? // Mohamed Elgendy // MLOps Podcast #229

MLOps.community · Intermediate ·📐 ML Fundamentals ·2y ago
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ MLOps podcast #229 with Mohamed Elgendy, Co-founder & CEO of Kolena Inc., What is AI Quality? // Abstract Delve into the multifaceted concept of AI Quality. Demetrios and Mo explore the idea that AI quality is dependent on the specific domain, equitable to the difference in desired qualities between a $1 pen and a $100 pen. Mo underscores the performance of a product being in sync with its intended functionality and the absence of unknown risks as the pillars of AI Quality. They emphasize the need for comprehensive quality checks and adaptability of standards to differing product traits. Issues affecting edge deployments like latency are also highlighted. A deep dive into the formation of gold standards for AI, the nuanced necessities for various use cases, and the paramount need for collaboration among AI builders, regulators, and infrastructure firms form the core of the discussion. Elgendy brings to light their ambitious AI Quality Conference, aiming to set tangible, effective, but innovation-friendly Quality standards for AI. The dialogue also accentuates the urgent need for diversification and representation in the tech industry, the variability of standards and regulations, and the pivotal role of testing in AI and machine learning. The episode concludes with an articulate portrayal of how enhanced testing can streamline the entire process of machine learning. // Bio Mohamed is the Co-founder & CEO of Kolena and the author of the book “Deep Learning for Vision Systems”. Previously, he built and managed AI/ML organizations at Amazon, Twilio, Rakuten, and Synapse. Mohamed regularly speaks at AI conferences like Amazon's DevCon, O'Reilly's AI conference, and Google's I/O. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.kolena.io Deep Learning for Vision Sys
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23 Hybrid Data Science Teams @SurveyMonkey
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25 Doing ML with Personal Information
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26 Evolution of the ML feature store @SurveyMonkey
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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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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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36 Learning from real life Machine Learning failures
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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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41 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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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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53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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