On Juggling, Dr. Seuss and Feature Stores for Real-time AI/ML // Nava Levy // MLOps Meetup #101

MLOps.community · Beginner ·🏗️ Systems Design & Architecture ·3y ago
MLOps Community Meetup #101! Last Wednesday we talked to Nava Levy, Developer Advocate for Data Science and MLOps of Redis. //Abstract Real-time ML-based applications are on the rise but deploying them at scale for large datasets with low latency and high throughput is challenging. This talk discusses the important role of feature stores for machine learning in deploying these applications. By exploring a number of use cases in production, we see how the choice of online data store and the feature store data architecture play important roles in determining its performance and cost. Throughout the presentation, Nava illustrates key points by connecting them to juggling and Dr. Seuss! Stay tuned :) // Bio Nava is a Developer Advocate for Data Science and MLOps at Redis. She started her career in tech with an R&D Unit in the IDF and later had the good fortune to work with and champion Cloud, Big Data, and DL/ML/AI technologies just as the wave of each of these was starting. Nava is also a mentor at the MassChallenge accelerator and the founder of LerGO—a cloud-based EdTech venture. In her free time, she enjoys cycling, 4-ball juggling, and reading fantasy and sci-fi books. // Jobs board https://mlops.pallet.xyz/jobs // Related links KDnuggets article: https://www.kdnuggets.com/2022/03/feature-stores-realtime-ai-machine-learning.html Feast with Redis Quickstart tutorial: https://redis.com/blog/feast-with-redis-tutorial-for-machine-learning/ Linkedin: https://il.linkedin.com/in/nava1 LerGO: www.lergo.org.il (focused on Hebrew, Arabic) LinkedIn's Feathr on Azure with Redis Slack: feathrAI.slack.com Slides: https://drive.google.com/file/d/1CXNBhgvGi16KPEuk2Nl5NsyQpgcXl99e/view?usp=sharing Linkedin's Feathr on Azure with Redis: https://github.com/linkedin/feathr/blob/main/docs/quickstart.md Feast with Redis quickstart: https://redis.com/blog/feast-with-redis-tutorial-for-machine-learning/ Feast on Azure with Redis: https://github.com/Azure/feast-azure Buil
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2 Remote Collaboration as a Data Scientist
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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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18 ML tooling in large companies
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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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21 Message buses, Async and sync architecture
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22 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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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
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
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30 MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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31 MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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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
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
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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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
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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54 Are Kubeflow and Airflow complementary?
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