11 lessons learned from doing deployments // Sol Rashidi // DE4AI

MLOps.community · Beginner ·📊 Data Analytics & Business Intelligence ·1y ago
//Abstract With over 200+ POCs built and with nearly 40 products in production, Sol walks us through the journey of developing AI products at scale and the 11 Lessons Learned in the journey - spoiler alert, only 30% of the challenges are tech related, 70% are non-tech issues! //Bio With eight (8) patents granted, 21 filed, and received awards that include: "Top 100 AI People" 2023 "The Top 75 Innovators of 2023" "Top 65 Most Influential Women in 2023" "Forbes AI Maverick of the 21st Century" 2022 “Top 10 Global Women in AI & Data”, 2023 "Top AI 100 Award", 2023 “50 Most Powerful Women in Tech”, 2022 “Global 100 Power List” - 2021, 2022, 2023 “Top 20 CDOs Globally” - 2022 "Chief Analytics Officer of the Year" - 2022 "Isomer Innovators of the Year" - 2021, 2022, 2023 "Top 100 Innovators in Data & Analytics” - 2020, 2021, 2022, 2023 "Top 100 Women in Business" - 2022 Sol is an energetic business executive and a goal-oriented technologist, skilled at coupling her technical acumen with story-telling abilities to articulate business value with both startups and Fortune 100's who are leaning into data, AI, and technology as a competitive advantage while wanting to preserve the legacy in which they were founded upon. Sol has served as a C-Suite member across several Fortune 100 & Fortune 500 companies including: Chief Analytics Officer - Estee Lauder Chief Data & Analytics Officer - Merck Pharmaceuticals EVP, Chief Data Officer - Sony Music Chief Data & AI Officer - Royal Caribbean Cruise Lines Sr. Partner leading the Digital & Innovation Practice- Ernsty & Young Partner leading Watson Go-To-Market & Commercialization - IBM Sol now serves as the CEO of ExecutiveAI LLC. A company dedicated to democratizing Artificial Intelligence for Humanity and is considered an outstanding and influential business leader who is influencing the space traveling the world as a keynote speaker, and serving as the bridge between established Gen1.0 markets and those evolving into 4.0. A bi
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1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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2 Remote Collaboration as a Data Scientist
Remote Collaboration as a Data Scientist
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3 MLOps Manifesto with Luke Marsden from Dotscience
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4 MLOps lifecycle description
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5 What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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6 Life purpose and too many spreadsheets
Life purpose and too many spreadsheets
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7 Explainability, Black boxes and EU white paper on reproducibility
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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16 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
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17 MLOps Problems in different size companies
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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
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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24 How do you handle ML version control at SurveyMonkey
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25 Doing ML with Personal Information
Doing ML with Personal Information
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26 Evolution of the ML feature store @SurveyMonkey
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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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
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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40 Who has the highest standards in ML?
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
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
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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54 Are Kubeflow and Airflow complementary?
Are Kubeflow and Airflow complementary?
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55 Why Kubeflow gained so much traction=open community
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56 Who decides the dirrection of Kubeflow
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57 What do Kubeflow and Arrikto do and how do they work together?
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58 Versioning your ML steps with Kubeflow
Versioning your ML steps with Kubeflow
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59 Machine Learning Lifecycles//Perception vs Reality
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