Building infra for real-time ML. We're hiring!
Teaching ML Sys Design @Stanford
Real-time ML survey: https://t.co/G88zDnyM7u
#MLOps
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Oct 7, 2020 • 5 tweets • 1 min read
Some asked me about concept drift so here you go.
A predictive ML model learns theta to output P(Y|X; theta).
Data drift is when P(X) changes: different data distributions, different feature space.
Ex: service launched in a new country, expected features becoming NaNs.
1/5
Label schema change is when Y changes: new classes, outdated classes, finer-grained classes. Especially common with high-cardinality tasks.
Ex: there’s a new disease to categorize.
2/5
Sep 29, 2020 • 6 tweets • 1 min read
When talking to people who haven’t deployed ML models, I keep hearing a lot of misperceptions about ML models in production. Here are a few of them.
(1/6)
1. Deploying ML models is hard
Deploying a model for friends to play with is easy. Export trained model, create an endpoint, build a simple app. 30 mins.
Deploying it reliably is hard. Serving 1000s of requests with ms latency is hard. Keeping it up all the time is hard.
(2/6)
Mar 7, 2020 • 5 tweets • 1 min read
I've been talking to a lot of people looking to join/having joined startups and I'm flabbergasted by how often people think joining startups is a get rich quick scheme. Here's the math why it doesn't work and what to look for when joining startups. (1/n)
Equity: anywhere 0.001% - 10%. A friend recently joined a 15-pax seed startup that offered 4%/4 years + lot of $. He'd be the ML engineer. They need him to raise A. It looks good on paper but do you want a company where you're clearly the best at what you want to learn? (2/n)
Oct 28, 2019 • 7 tweets • 2 min read
To learn how to design machine learning systems, I find it really helpful to read case studies to see how great teams deal with different deployment requirements and constraints. Here are some of my favorite case studies.
Topics covered: lifetime value, ML project workflow, feature engineering, model selection, prototyping, moving prototypes to production. It's completed with lessons learned and looking ahead!
To better understand the technical hiring pipelines, I analyzed 15,897 interview reviews for 27 major tech companies on Glassdoor. I focused on interviews for software engineering related roles, both junior and senior levels. These are some of the main findings. (1/n)
Each review consists of:
- result (no offer/accept offer/decline offer)
- difficulty (easy/medium/hard)
- experience (positive/neutral/negative)
- review (application/process/questions)
The largest SWE employers are Google, Amazon, Facebook, and Microsoft.
Aug 3, 2019 • 11 tweets • 10 min read
This thread is a combination of 10 free online courses on machine learning that I find the most helpful. They should be taken in order.
1. Probability and Statistics by Stanford Online
This self-paced course covers basic concepts in probability and statistics spanning over four fundamental aspects of machine learning: exploratory data analysis, producing data, probability, and inference. online.stanford.edu/courses/gse-yp…
Jul 19, 2019 • 18 tweets • 3 min read
I'm working on a book on machine learning interviews so I've been spending the last few months talking to companies about their hiring process for ML roles. This thread is a summary of what I've learned. It will be updated as the book progresses. (1/n)
The average interviewer gets very little training. You start your full-time job. You shadow a few interviews. Then you're on your own. As a result, interviews are wildly different even within the same company.
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