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DataTalkChill3 - Intro to MLOps

DataTalkChill3 - Intro to MLOps

Introduction to MLOps: What and Why
By Fon, Kamolphan Liwprasert

Presented at DataTalkChill #3 meet up by DataTH.com on Mar 28th, 2021

Note: some part of the slide is in Thai language.

Kamolphan Liwprasert

March 28, 2021

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  1. about:me FON Kamolphan Liwprasert Senior Machine Learning Engineer @Sertis Master

    in Computer Science student @GeorgiaTech Road to Data Engineer instructor @DataTH
  2. https://youtu.be/VfcY0edoSLU นิยาม AI System แบบงาย ๆ AI System = *

    Software Engineering DevOps Data Engineering Data Science ML/AI Research ML Engineering Code + Model + DATA
  3. 3 Levels of ML Software 1. Data ▸ Data Engineering

    Pipelines ▸ Data Quality is a key 🔑 2. Model ▸ Machine Learning Pipelines ▸ Train, Evaluate, Test 󰙤 3. Code ▸ Model Serving & Predictions ▸ Deployment strategies & Infra 🐳☁
  4. MLOps Continuous Training Continuous Monitoring CI/CD Concept drift Logging Canary

    Deployment A/B Testing Data-Centric AI vs Model-Centric AI Monitoring Healthcheck Testing Data Version Control Experiment Tracking Data Lineage Lifecycle Automation Data Quality Data Labeling Data Augmentation Train & Evaluate Model Versioning Model Serving & Deployment Optimization Error Reporting
  5. To make great products: do machine learning like the great

    engineer you are, not like the great machine learning expert you aren’t. Martin Zinkevich https://developers.google.com/machine-learning/guides/rules-of-ml
  6. 1 2 3 4 Benefit of MLOps Automate process ชวยลด

    Technical Debt ในระยะยาว ได Pipeline ที่ดี ทําให data scientist ทํางานรวมกันไดงาย ชวยลดความเสี่ยง และขอผิด พลาด human error ปรับปรุง และ Maintain ได งาย สามารถ scale ได
  7. Concerns • แตละ tool มี usecase ที่เหมาะสมของเครื่องมือแตละอยาง ⚒ • Pricing

    $$$$ บาง tool มีคาใชจายคอนขางสูง 💸 • Over-engineering : Perfect is the enemy of good
  8. References & Resources • MLOps https://ml-ops.org • Introducing MLOps, Mark

    Treveil and team (O’Reilly Media). • Rules of Machine Learning, Martin Zinkevich https://developers.google.com/machine-learning/guides/rules-of-ml • Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf • What Is MLOps?, Nvidia https://blogs.nvidia.com/blog/2020/09/03/what-is-mlops/ • A Chat with Andrew on MLOps: From Model-centric to Data-centric AI, Andrew Ng https://youtu.be/06-AZXmwHjo • Let’s talk about MLOps, Christian Barra https://youtu.be/K5x6dxjY1vA • MLOps: Continuous delivery and automation pipelines in machine learning, Google Cloud https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation -pipelines-in-machine-learning • Awesome-mlops, visenger (on GitHub) https://github.com/visenger/awesome-mlops • CML, powered by DVC https://cml.dev https://dvc.org
  9. Thank you 😃 Feedback is a gift 🎁 bit.ly/feedback-form-fon Sertis

    is hiring 󰠁 bit.ly/sertis-apply-now Road to Data Engineer course 🎓 bit.ly/road2de-datatalk *ลด 5% ถึงเที่ยงคืนวันที่ 28 มี.ค.นี้