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apply conf(2022)

Created
May 25, 2022 7:53 AM
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Here are some of my notes from the apply(conf) 2022!
For every presentation, I tried to write the most critical pieces of information that I would be able to remember afterwards; think of it as a low-pass filter of understanding. I also tried to include as many slides as I could find on the Slack of the event, please feel free to send me additional ones.

Day 1

🌅
Morning
1. Managing the flywheel, Mike del Balso, Tecton
2. Lakehouse: a new kind of platform, Matei Zaharia, Databricks
3. ML for online prediction, Chip Huyen, Claypot AI
⚡
Lightening talks round 1
4. Why is ML hard? Tal Shaked, Snowflake
5. DIY minimal feature store, João Santiago, Billie
6. Enabling rapid model deployment in healthcare, Felix Brann, Vital
7. Extending Open Source feature Store, Joost & Thijs, Adyen
8. Compass: Composable & Scalable Signal Engineering, Justin Young, Abnormal Security
9. Streaming is an implementation detail, Arjun Narayan, Materialize
10. Effective system ML development, Leonard Aukea, Volvo
🕶️
Afternoon
11. How to draw an owl and build effective ML stacks, Sarah Catanzaro, Amplify
12. Panel: What engineers should know when building for data scientists?
13. Is production RL at a tipping point? Waleed Kadous, Anyscale
⚡
Lightening talks round 2
14. Declarative ML Systems and Ludwig, Pierre Molino & Travis Addair, Predibase
15. Accelerating model deployment velocity, Emmanuel Ameisen, Stripe
16. Semantic Layers & Feature stores, Drew Banin, dbt Labs
17. Engineering for applied ML, Yuchen Wu, Instacard
18. PyTorch’s next generation of data tooling, Donny Greenberg, PyTorch at Meta

Day 2

🌅
Morning
19. Faire’s journey toward modern data and ML stack, Daniele Perito, Faire
20. Cash App’s real-time ranking ML system, Meenal & Austin, Block Inc.
21. Feature engineering at scale with Dagger and Feast, Ravi Suhag, Gojek
⚡
Lightening talks round 3
22. Data observability for ML team, Kyle Kirwan, Bigeye
23. ML meet SQL, Dan Sullivan, 4 Mile Analytics
24. Learning from monitoring more than 30 ML use-cases, Lina Weichbrodt,
25. Lessons learned from working on Feast, Willem Pienaar, Tecton
26. Evaluating RecSys in production, Federico, Tooso
🕶️
Afternoon
27. Fire chat: Is ML a subset or superset of programming? Mike & Martin
28. Panel: Alexander Ratner & Aparna Dhinakaran & Ketan Umare & Biswaroop Palit
29. Intelligent Customer Preference Engine with Real-time ML systems, Manoj & Praveen, Walmart Lab
⚡
Lightening talks round 4
30. Are transformers becoming the most impactful tech of the decade? Clément Delangue, Hugging Face
31. Training large scale recommendation models with TPU, Aymeric Damien, Snap
32. Streamlining NLP model creation and inference, Cary & Phillip, Primer.ai
33. Real-time, accuracy and lineage-aware featurization, Sarah Wooders, Sky (ralf)
34. Making model cards, Chris Albon, Wikimedia

Workshops