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2022-12-15

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All Talks Recorded and Shared!

🗒️ Details 🗒️

Thursday, 15 Dec 2022

Online

Cost: FREE

12:30 UTC - 04:00 UTC

07:30 EST - 23:00 EST

Full schedule

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🗓️ Schedule 🗓️

Session 1
UTC	EST	PDT	JST	Speaker	Talk Title	Transcript
12:30	7:30 AM	4:30 AM	21:30	Vicki Boykis	Keynote
13:00	8:00 AM	5:00 AM	22:00	Vincent D. Warmerdam	Group-by statements that save the day
13:30	8:30 AM	5:30 AM	22:30	Tim Hopper	Five semesters of linear algebra and all I do is solve Python dependency problems
14:00	9:00 AM	6:00 AM	23:00	Lynn Cherny	NLP tips and tricks
14:30	9:30 AM	6:30 AM	23:30	Matthijs Brouns	How small can I get that Docker container?
15:00	10:00 AM	7:00 AM	00:00	Guenia Izquierdo	Spark horror stories from the field
15:30	10:30 AM	7:30 AM	00:30	Luca Belli	Geriatric data science: life after senior
16:00	11:00 AM	8:00 AM	01:00	Helena Sarin	A Game of Construction
16:30	11:30 AM	8:30 AM	01:30	Zachary Blackwood	Hack your way to a better API

Session 2
UTC	EST	PDT	JST	Speaker	Talk Title	Transcript
17:00	12:00 PM	9:00 AM	02:00	Joel Grus	What's the simplest possible thing that might work, and why didn't you try that first?
17:30	12:30 PM	9:30 AM	02:30	Peter Sobot	It's all about cost: how to think about machine learning products
18:00	1:00 PM	10:00 AM	03:00	Jeremy Jordan	ML doesn't always replace rules, sometimes they work together
18:30	1:30 PM	10:30 AM	03:30	Shreya Shankar	All my machine learning problems are actually data management problems
19:00	2:00 PM	11:00 AM	04:00	Jowanza Joseph	Three cheers for OpenAPI 3
19:30	2:30 PM	11:30 AM	04:30	Ethan Rosenthal	Ethan Rosenthal and the M1 misadventure
20:00	3:00 PM	12:00 PM	05:00	Peter Baumgartner	Data is the new o̶i̶l̶ coffee
20:30	3:30 PM	12:30 PM	05:30	Katie Bauer	How to translate to PM speak and back
21:00	4:00 PM	1:00 PM	06:00	Caitlin Hudon	Tracer bullets + working backwards: simple framework for solving problems
21:30	4:30 PM	1:30 PM	06:30	Sean Taylor	When not to use SQL

Session 3
UTC	EST	PDT	JST	Speaker	Talk Title	Transcript
22:00	5:00 PM	2:00 PM	07:00	☕ Coffee/Nutella Break 🌰🍫	☕ Take a break! 🍵🚽
22:30	5:30 PM	2:30 PM	07:30	Ben Labaschin	Building an HTTPS Model API for Cheap: AWS, Docker, and the Normconf API
23:00	6:00 PM	3:00 PM	08:00	James Kirk	Data's desire paths: shortcuts and lessons from industrial recommender systems
23:30	6:30 PM	3:30 PM	08:30	Julia Silge	How many folds is too many? Efficient simulation for everyday ML decisions
00:00	7:00 PM	4:00 PM	09:00	Brandon Rohrer	The zen of tedium
00:30	7:30 PM	4:30 PM	09:30	Roy Keyes	Data ethics: the non-sexy parts
01:00	8:00 PM	5:00 PM	10:00	Brianna McHorse	How should I represent the intermediate thing? Python data objects for normcore ML
01:30	8:30 PM	5:30 PM	10:30	JD Long	I'd have written a shorter solution but I didn't have the time
02:00	9:00 PM	6:00 PM	11:00	Juliet Hougland	The physics of data
02:30	9:30 PM	6:30 PM	11:30	Josh Wills	Just use one big machine for model training and inference
03:00	10:00 PM	7:00 PM	12:00	Rose Wiegley	Data-Driven promotions
03:30	10:30 PM	7:30 PM	12:30	Chris Albon	Don't do invisible work

⚡️ Lightning Talks ⚡️

YouTube Playlist Now Here!

Click on each talk title to view it individually

Speaker	Talk Title
Amanda Alvarez	Minimizing the Cost Function in Data Projects (or, Keep it Simple, Stupid)
Amanda Fioritto	Qualify: The SQL Filtering Pattern You Never Knew You Needed
Anna Godwin	Intro to PDF Text & Table Extraction
Anuvabh Dutt	Config files for fast and reproducible ML experiments
Aviel Makmal	You Don't Need ML for That Problem
Bryan Bischof	Toss that(model) in an endpoint
Catherine Nelson	Dull, Dry and Indispensable: Documentation Do's and Don'ts for Data Scientists
Chelsea Parlett	Why Are You The Way That You Are: Sklearn Quirks
Georg Meyer	A Case for Beauty in IT
Izzy Miller	Have you tried rubbing a hash on it?
Jacqueline Nolis	Alaska challenged my preconceived notions of storing sunset data
Jane Adams	How to Make Six Figures an Hour
Jenny Bryan	How to name files like a normie
Joe Lucas	IPython Profiles: Big Bag o’ Functionality
Juulia Suvilehto	Trying to convince academics to use git
Kelly Bodwin	The Accuracy is Too Damn High!
Kevin H. Wilson	Model Evaluation in COBOL
Lucila Stancato	The night I accidentally killed a Kubernetes service and realized 3 days later
Luis Müller	Metrics Are Born at Sea But Stored In the Cloud
Nicolò Giso	random.randint(5, 12) lessons I learned as a Data Scientist in 5 years in industry
Rachel Bunder	Much Ado About Nothing
Randy Au	Everything is on fire and you get to contribute
Sebastián Pérez Saaibi	Is "Data Happiness" even Possible? A tale of Product Analytics as a Product
Shoili Pal	Data Science Intake Forms
Sophia Yang	PyScript: Run Python in your HTML
Teresa Kubacka	How to stop crying when using Matplotlib
TJ Murphy	Hell is other people's bugs
Tom Baldwin	Putting Git's commit hash in __version__, two ways
Trish Gillett	Data goalie: keeping pucks out of the net
Victor Geislinger	Persistent SSH Sessions w/ tmux
Zachary Chetchavat	Hotkeys for Spreadsheets Cookbook Practical Solutions from CTRL-Arrow, to F4

🚪Hallway Track 🚪

YouTube Playlist

Click on each talk title to view it individually

Speaker	Talk Title
Taylor Murphy	The Best Data Pipeline is the One You Do By Hand
Tobias Brandt	PRQL: A modern language for transforming data

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📖 About 📖

NormConf is the tech conference about all the stuff that matters in data and machine learning, but doesn't get the spotlight.

It was once said that a single tweet could launch a thousand ships, and so it was for NormConf:

What if there were a conference about all the mundane, behind-the-scenes, how-the-sausage-is-made, middlebrow, unsexy, normcore stuff in the data and ML parts of the tech world?