NYC Data #346: MLOps vs ML Platform, Shubert Theatre, Valentine's Day Data, Lattice, Tandem, Paramount, Clickhouse's Optimizations, Barstool Sports
Plus, o3's costs!
Hi friends, somehow this is my last letter for the month of April! I have been enjoying the beautiful weather and I’m really looking forward to the May flowers 🌼 (speaking of which, this is the last weekend for the Orchid Show)!
As always, help me keep this space up-to-date: please send me posts, events, and job openings. If you know someone who might enjoy or benefit from this newsletter, please share it with them. [image credit: NYBG]
Good Local Posts
Paul Yang at Runhouse had an interesting post on MLOps vs ML Platform, comparing them to more traditional DevOps / Platforms, and calling out some ML specific aspects.
The MTA team did a fun deep dive into how NYC traffic changes on Valentine’s Day. Pro tips: if you don’t like crowds & traffic, avoid Times Square & the Verrazzano!
Due to the measles outbreak, The Upshot published an interactive herd immunity simulator. Fun to play with, sad that this is happening in 2025.
Upcoming In-Person Events (new listings in bold)
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4/28: Open Data Journey - Motor Vehicle Collisions
4/29: Foundations of Data Science Workshop
5/8: New York Summer Data Summit
5/10: SQL Saturday New York City
5/15: AI Summit NYC: The Technology Conference For Non-Tech Professionals
5/15: Data Science Salon New York
5/21: Global GRC, Data Privacy & Cyber Security ConfEx
5/28 - 5/30: Lifetime Data Science Conference
6/2 - 6/8: NY Tech Week
6/5: Generative AI Summit
Open Roles
Squarespace is hiring for several data roles, including a Senior Data Scientist and multiple Data Analysts.
Tandem is hiring ML Engineers and other roles.
Lattice is looking for a Senior Data Scientist.
Paramount is seeking a Data Engineer for its Applied Intelligence Personalization Team.
Barstool Sports is hiring a Data Engineer.
The Shubert Organization (America’s oldest theatre company) is seeking a Data Director.
Miscellany
A wild fact about GPT-o3's extreme costs on the ARC-AGI Test (here’s a sample problem). The model wrote 44 million words to answer these, on average!
Tom Schreiber of Clickhouse discussed performance optimizations (PREWHERE & lazy materialization) with some gifs to help illustrate what’s happening under the hood. Really helpful!
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