Vincent MaladiereProba MLProba MLApply Conf 2022Apply Conf 2022Contributing to scikit-learnContributing to scikit-learnOpenCV TutorialOpenCV TutorialAboutAboutContactContact
  • Home
  • Day 1 - 🌅 Morning
  • 1. Managing the flywheel
  • 2. Lakehouse a new kind of platform
  • 3. ML for online prediction
  • Day 1 - ⚡ Lightning talks 1
  • 4. Why is ML hard
  • 5. DIY minimal feature store
  • 6. Enabling rapid model deployment in healthcare
  • 7. Extending Open Source feature Store
  • 8. Compass Composable & Scalable Signal Engineering
  • 9. Streaming is an implementation detail
  • 10. Effective system ML development
  • Day 1 - 🕶️ Afternon
  • 11. How to draw an owl and build effective ML stack
  • 12. Panel What engineers should know when building
  • 13 Is production RL at a tipping point
  • Day 1 - ⚡ Lightning talks 2
  • 14. Declarative ML Systems and Ludwig
  • 15. Accelerating model deployment velocity
  • 16. Semantic Layers & Feature stores
  • 17. Engineering for applied ML
  • 18. PyTorch’s next generation of data tooling
  • Day 2 - 🌅 Morning
  • 19. Faire’s journey toward modern data and ML stack
  • 20. Cash App’s real-time ranking ML system
  • 21. Feature engineering at scale with Dagger and Feast
  • Day 2 - ⚡ Lightning talks 1
  • 22. Data observability for ML team
  • 23. ML meet SQL
  • 24. Learning from monitoring more than 30 ML use-cases
  • 25. Lessons learned from working on Feast
  • 26. Evaluating RecSys in production
  • Day 2 - 🕶️ Afternoon
  • 27. Fire chat Is ML a subset or superset of programming
  • 28. Panel Alexander Ratner & Aparna Dhinakaran
  • 29. Intelligent Customer Preference Engine
  • Day 2 - ⚡ Lightning talks 2
  • 30. Are transformers becoming the most impactful
  • 31. Training large scale recommendation models
  • 32. Streamlining NLP model creation and inference
  • 33. Real-time, accuracy and lineage-aware featurization
  • 34. Making model cards
Question? Give us feedback → (opens in a new tab)Edit this page
Apply Conf 2022
4. Why is ML hard

4. Why is ML hard? Tal Shaked, Snowflake

https://www.youtube.com/watch?v=ezqsNzwN0p4 (opens in a new tab)

👉 Slides (opens in a new tab)

Some history about ML at Google in 2005 and onwards

3. ML for online prediction5. DIY minimal feature store