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.

Transcript

1. MLOps คืออะไร ? ทําไมคนถึงวาดี Kamolphan Liwprasert (Fon)
2. about:me FON Kamolphan Liwprasert Senior Machine Learning Engineer @Sertis Master in Computer Science student @GeorgiaTech Road to Data Engineer instructor @DataTH
3. MLOps คืออะไร ?
4. ML Machine Learning Operations Ops https://neptune.ai/blog/end-to-end-mlops-platforms
5. 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
6. MLOps แตกตางจาก DevOps อยางไร? DevOps MLOps
7. 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 🐳☁
8. 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
9. ทําไม ตองมี MLOps ? ? ? ?
10. ทําไมคนเพิ่งมาพูดถึง MLOps
11. https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning Hidden Technical Debt in Machine Learning Systems Elements for ML systems
12. Rule of Machine Learning Martin Zinkevich https://developers.google.com/machine-learning/guides/rules-of-ml
13. 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
14. 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
15. เริ่มตนกับ MLOps ยังไงดี?
16. Tools DVC (Data Version Control)
17. Cloud Platforms Azure Machine Learning Amazon SageMaker Google Cloud AI Platform
18. 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
19. เริ่มตนกับ MLOps - Beginner edition Empathize เรียนรูจาก pain Git VersionControl Docker Kubernetes CI/CD Programming!
20. RECAP
21. https://ml-ops.org/content/mlops-principles
22. 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
23. Q & A 💬
24. 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