Chip Huyen
Engineer and writer on machine-learning systems; author of Designing Machine Learning Systems and AI Engineering. "I work to bring AI into production. I write about AI system design."
Chip Huyen did not write this page. ppll compiled it from their own words, each linked to the source. Is this you? · Report a mistake
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What they believe
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As AI products converge on the same underlying models, product design becomes the real differentiator, not the model itself.
Because everyone uses the same models nowadays, the AI components of AI products are similar, and the differentiation is product.
huyenchip.comSaid 16 Jan 2025This on korrents
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Changed their mind Kubernetes is an essential skill for data scientists and ML engineers to learn
Before Things I’d prioritize learning if I was to study to become a ML engineer again: 1. Version control 2. SQL + NoSQL 3. Python 4. Pandas/Dask 5. Data structures 6. Prob & stats 7. ML algos 8. Parallel computing 9. REST API 10. Kubernetes + Airflow
Now When I wrote that tweet, I believed that Kubernetes was essential to the DS/ML workflow. This sentiment came from the frustration at my own job – my life as an ML engineer would’ve been much easier if I was more fluent with K8s. However, as I learned more about low-level infrastructure, I realized how unreasonable it is to expect data scientists to know about it.
Said 13 Sept 2021This on korrents
The bold line is korrents’ wording; the quote is theirs. These one: what they wrote before what they said aloud, one per source, newest first.
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29 March
From one piece Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit 14 beliefs · youtube.com
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Their words
And the question nowadays is less about like how to build because if you can describe the problem in the solution you want, usually AI can do it. Maybe not today, but maybe like two or three years from now on they can do a lot of those. The question is like what to build because we talk about like yes, if something exists, right? AI can replicate it.
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korrents.com
People no longer read books the way books assume they will — they jump around, whatever they tell the author.Their words
Like I I write books and as much I wish that books as a format will be like will survive. I do think that people don't read books the same way anymore.
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Their words
And I feel like it's a very human way of doing web search, right? Because we enter things in the Google and we see all the like citations, the quotations part. But like why would we limit AI to that? If AI already visit a page, why don't just pull the entire page out?
+ 11 more
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Their words
You cannot just reverse that, right? It's just not working. So, I do think that we need to build out the whole guardrails for the reversibility of actions because that actually where things get really really scary.
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Their words
So, I think that the whole workflow of like reviewing code is very outdated. Like, I don't think the I think that the senior member, instead of like giving feedback on the code, they should be giving feedback on like how you give instruction to AI to produce better.
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Their words
So, I I think there's a lot of things we still try to imagine. What would be an AI-driven world look like, right? I think people are trying to like retrofit what already exists to fit what they think is a new workflow.
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Their words
But in Vietnam and also in a lot of other Asian countries, people are on the move all the time. Like people are on the motorbike all the time. So, they actually really don't like typing. So, the voice like a lot of the companies in Vietnam actually deploy voice bots before they do uh do chatbot.
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Their words
I don't think that human preference is just an equation that people can just like package nicely and like, "Hey, ask people like which which of these two answers people would prefer?" It's very very personal, very culturally dependent, geographically dependent, age dependent.
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korrents.com
The problems worth a small builder's time are the ones too small for a big AI company to be motivated to solve.Their words
Like if it's a big problem, like everyone can see, then all these big companies will get into it. But whereas it's like there are a lot of problems that's like smaller, then maybe OpenAI won't be motivated to solve it, but maybe I can like all a lot of people can.
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korrents.com
Problems follow a long tail: AI will keep getting better at the common ones, and the edge cases will never go away.Their words
So, I think of them as the top of the long long tail long tail problem. Like, something very common issues that a lot of people experience. AI will get really good at it. And over time AI will cover more more edge cases, but the edge cases will never go away.
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Their words
The product you build is just solving a problem. So, is that like is the more you do that, the better you become at problem solving. And one thing I do believe that it will never change that there will always be problems to solve.
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Their words
And the more you build, right? The more you put things out there, you remove the need for imagination. You can say, "Okay, now I like that website. Do that for me."
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Their words
if you can describe a style, AI can generate it. And the same with software. If you can describe a software, then AI can build it for you.
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Their words
I feel like now I can build anything I want. But at the same time, anyone can build anything I want. So, what is the incentive structure for me to do anything?
11 February
29 January
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4 December 2025
30 October 2025
23 October 2025
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Recommendsrcmnd.app
The Selfish GeneTheir words
So one thing is a selfish gene that's like understand uh it actually changed uh it actually helped me with the question like whether I want to have kids or not.
8 August 2025
21 March 2025
16 January 2025
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Their words
Because everyone uses the same models nowadays, the AI components of AI products are similar, and the differentiation is product.
2 February 2024
8 May 2023
15 April 2023
27 December 2022
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Their words
I spend a lot of time thinking about modeling computational systems, and this book introduced me to modeling social systems.
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Their words
Clarke is best known for his science fiction (2001: A Space Odyssey), but his science nonfiction is fascinating too.
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Likedrcmnd.app
Fads and Fallacies in the Name of ScienceTheir words
Despite being written 70 years ago, the book is still insanely relevant today.
+ 8 more
-
Recommendsrcmnd.app
Fooled by Randomness: The Hidden Role of Chance in Life and in the MarketsTheir words
Taleb’s way of writing probably upsets a lot of people, but if you can get over it, it’s definitely worth a read.
-
Lovedrcmnd.app
How Not to Be Wrong: The Power of Mathematical ThinkingTheir words
A wonderful, easy-to-read introduction to applied math and probability.
-
Likedrcmnd.app
Uncommon Genius: How Great Ideas are BornTheir words
A collection of interviews with 40 recipients of the MacArthur Fellowship (the genius award) gave me a different perspective on the creative process.
-
Recommendsrcmnd.app
The Design of Everyday ThingsTheir words
A must read for anyone who cares about functional design, from the first User Experience Architect at Apple.
-
Likedrcmnd.app
Just My Type: A Book About FontsTheir words
We know that fonts can influence how we perceive that text. This book explains why and how we can better utilize this power.
-
Likedrcmnd.app
The Making of the Atomic BombTheir words
A lengthy but fascinating book on the development of nuclear weapons, which has a lot in common with the development of AI today.
-
Lovedrcmnd.app
Chaos: Making a New ScienceTheir words
I remember being blown away the first time I read the book.
-
Likedrcmnd.app
The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the InternetTheir words
An amazing overview to the history of cryptography.
16 November 2021
13 September 2021
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Their words, before
Things I’d prioritize learning if I was to study to become a ML engineer again: 1. Version control 2. SQL + NoSQL 3. Python 4. Pandas/Dask 5. Data structures 6. Prob & stats 7. ML algos 8. Parallel computing 9. REST API 10. Kubernetes + Airflow
Their words now
When I wrote that tweet, I believed that Kubernetes was essential to the DS/ML workflow. This sentiment came from the frustration at my own job – my life as an ML engineer would’ve been much easier if I was more fluent with K8s. However, as I learned more about low-level infrastructure, I realized how unreasonable it is to expect data scientists to know about it.
22 December 2020
11 November 2020
27 December 2019
Nothing matches.
Beliefs
Korrents What they believe 37 beliefs, 1 changed mind — each backed by an exact quote.
Each is a — compiled by korrents.com, not by them: the one-line wordings are korrents', the quotes are theirs.
Recent
Because anyone can now rebuild whatever you make, the ordinary incentive to build software has been knocked out from under us.
I feel like now I can build anything I want. But at the same time, anyone can build anything I want. So, what is the incentive structure for me to do anything?
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
Anything a person can describe, AI can now produce — software has reached the point image generation reached with style.
if you can describe a style, AI can generate it. And the same with software. If you can describe a software, then AI can build it for you.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
Every product put into the world removes the need for imagination, because it becomes something anyone can point AI at and copy.
And the more you build, right? The more you put things out there, you remove the need for imagination. You can say, "Okay, now I like that website. Do that for me."
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
Show 34 more
The reason to keep building is that building is practice at problem-solving, and the supply of problems will never run out.
The product you build is just solving a problem. So, is that like is the more you do that, the better you become at problem solving. And one thing I do believe that it will never change that there will always be problems to solve.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
Problems follow a long tail: AI will keep getting better at the common ones, and the edge cases will never go away.
So, I think of them as the top of the long long tail long tail problem. Like, something very common issues that a lot of people experience. AI will get really good at it. And over time AI will cover more more edge cases, but the edge cases will never go away.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
The problems worth a small builder's time are the ones too small for a big AI company to be motivated to solve.
Like if it's a big problem, like everyone can see, then all these big companies will get into it. But whereas it's like there are a lot of problems that's like smaller, then maybe OpenAI won't be motivated to solve it, but maybe I can like all a lot of people can.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
Human preference is not an equation you can package by asking which of two answers people prefer; it is personal, cultural, geographic and generational.
I don't think that human preference is just an equation that people can just like package nicely and like, "Hey, ask people like which which of these two answers people would prefer?" It's very very personal, very culturally dependent, geographically dependent, age dependent.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
Which interface an AI product should have is a cultural fact, not a technical one — Vietnamese companies ship voice bots before chatbots because people are on motorbikes.
But in Vietnam and also in a lot of other Asian countries, people are on the move all the time. Like people are on the motorbike all the time. So, they actually really don't like typing. So, the voice like a lot of the companies in Vietnam actually deploy voice bots before they do uh do chatbot.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
We are still retrofitting the tools we already had onto workflows we have only imagined, instead of designing for how people will actually work with AI.
So, I I think there's a lot of things we still try to imagine. What would be an AI-driven world look like, right? I think people are trying to like retrofit what already exists to fit what they think is a new workflow.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
Line-by-line code review has become an outdated ritual: senior engineers should be giving feedback on how their team instructs AI, not on the code AI wrote.
So, I think that the whole workflow of like reviewing code is very outdated. Like, I don't think the I think that the senior member, instead of like giving feedback on the code, they should be giving feedback on like how you give instruction to AI to produce better.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
The frightening part of giving agents more access is not bad code but irreversible action, so the guardrails to build next are guardrails on reversibility.
You cannot just reverse that, right? It's just not working. So, I do think that we need to build out the whole guardrails for the reversibility of actions because that actually where things get really really scary.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
AI web search is imitating a human habit for no reason — revisiting the same pages for snippets instead of taking the whole page once.
And I feel like it's a very human way of doing web search, right? Because we enter things in the Google and we see all the like citations, the quotations part. But like why would we limit AI to that? If AI already visit a page, why don't just pull the entire page out?
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
People no longer read books the way books assume they will — they jump around, whatever they tell the author.
Like I I write books and as much I wish that books as a format will be like will survive. I do think that people don't read books the same way anymore.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
The hard question is no longer how to build something but what to build, because anything already described can be replicated.
And the question nowadays is less about like how to build because if you can describe the problem in the solution you want, usually AI can do it. Maybe not today, but maybe like two or three years from now on they can do a lot of those. The question is like what to build because we talk about like yes, if something exists, right? AI can replicate it.
Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit Said 29 Mar 2026
As AI products converge on the same underlying models, product design becomes the real differentiator, not the model itself.
Because everyone uses the same models nowadays, the AI components of AI products are similar, and the differentiation is product.
Common pitfalls when building generative AI applications Said 16 Jan 2025
Building a working AI demo is far easier than turning it into a reliable production product.
This is perhaps one of the first painful lessons anyone who has built an AI product quickly learns. It's easy to build a demo, but hard to build a product.
Common pitfalls when building generative AI applications Said 16 Jan 2025
AI judges used to evaluate AI outputs need ongoing validation and iteration, just like any other AI system.
AI judges must be evaluated and iterated over time, just like all other AI applications.
Common pitfalls when building generative AI applications Said 16 Jan 2025
Planning is fundamentally a search problem: explore paths, predict outcomes, and pick the best one.
Planning, at its core, is a search problem. You search among different paths towards the goal, predict the outcome (reward) of each path, and pick the path with the most promising outcome.
Agents Said 7 Jan 2025
Most real-world AI agent systems are actually multi-agent systems made of multiple components.
Because most agentic workflows are sufficiently complex to involve multiple components, most agents are multi-agent.
Agents Said 7 Jan 2025
A self-driving car is more trustworthy than a random human driver.
Personally, I would trust a self-driving car more than the average stranger to give me a lift.
Agents Said 7 Jan 2025
Preparing context for a language model plays the same role that feature engineering played for classical machine learning models.
Context construction for foundation models is equivalent to feature engineering for classical ML models.
Building A Generative AI Platform Said 25 Jul 2024
In-context learning is a form of continual learning for language models.
In-context learning, learning from the context, is a form of continual learning.
Building A Generative AI Platform Said 25 Jul 2024
The probabilistic nature of AI models makes their outputs inherently unreliable.
AI models are probabilistic, making their outputs unreliable.
Building A Generative AI Platform Said 25 Jul 2024
A person's life tends to change character in cycles of roughly three to six years.
I have this theory that life has a circadian rhythm. Every 3-6 years, you become a different person.
Measuring personal growth Said 17 Apr 2024
Growing older tends to expand rather than shrink the number of achievable dreams and options.
As I grow older, I have more dreams. I now know many things that I didn't know before, and I have access to more resources than I ever did.
Measuring personal growth Said 17 Apr 2024
Under uncertainty, the best decision is the one that preserves the most future options.
In the face of uncertainty, I lean towards the decision that would give me the most future options.
Measuring personal growth Said 17 Apr 2024
The lower a layer sits in the software stack, the harder it is for an individual developer to build in it.
the lower we go in the stack, the harder it is for individuals to build.
What I learned from looking at 900 most popular open source AI tools Said 14 Mar 2024
AI will enable one-person companies to reach very high valuations.
Several people have speculated that we'll see many very valuable one-person companies (see Sam Altman's interview and Reddit discussion). I think they might be right.
What I learned from looking at 900 most popular open source AI tools Said 14 Mar 2024
Routing prompts to different AI models can improve response quality while lowering cost and latency.
Model routing has the potential to increase response quality while reducing costs and latency.
Predictive Human Preference: From Model Ranking to Model Routing Said 28 Feb 2024
As more AI models are developed with varying capabilities and costs, routing between them carries real economic value.
With more and more models being developed, each with different capabilities and a cost structure, model routing has clear economic values.
Predictive Human Preference: From Model Ranking to Model Routing Said 28 Feb 2024
Weaker AI models match stronger ones on simple prompts, but strong models are increasingly preferred as prompts get more challenging.
One pattern is that for simple prompts, weak models can do (nearly) as well as strong models. For more challenging prompts, however, users are much more likely to prefer stronger models.
Predictive Human Preference: From Model Ranking to Model Routing Said 28 Feb 2024
The probabilistic nature of AI models is what makes them well suited to creative tasks
This probabilistic nature makes AI great for creative tasks.
Generation configurations: temperature, top-k, top-p, and test time compute Said 16 Jan 2024
When a model behaves unreliably, the better fix is to switch models rather than repeatedly sample it to work around the inconsistency
The optimal thing to do with a fickle model, however, is to swap it out for another.
Generation configurations: temperature, top-k, top-p, and test time compute Said 16 Jan 2024
As models get better at following instructions, techniques like finetuning and constrained sampling for structured outputs will become less necessary
I suspect that in the future, it'll be easier to get models to output exactly what we need with minimal prompting, and these techniques will become less important.
Generation configurations: temperature, top-k, top-p, and test time compute Said 16 Jan 2024
Text is a more powerful output modality for a model than images, because text supports many downstream tasks
Text is a much more powerful mode for model outputs. A model that can generate images can only be used for image generation, whereas a model that can generate text can be used for many tasks: summarization, translation, reasoning, question answering, etc.
Multimodality and Large Multimodal Models (LMMs) Said 10 Oct 2023
Image is the most versatile input modality for a model, since it can represent text, tabular data, and audio
Image is perhaps the most versatile format for model inputs, as it can be used to represent text, tabular data, audio, and to some extent, videos. There's also so much more visual data than text data.
Multimodality and Large Multimodal Models (LMMs) Said 10 Oct 2023
A model that could learn effectively from raw bitstrings or bytestrings would be extremely powerful, able to learn from any data modality
A model that can effectively learn from bitstrings or bytestrings will be very powerful, and it can learn from any data mode.
Multimodality and Large Multimodal Models (LMMs) Said 10 Oct 2023
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Recommends rcmnd
rcmnd What they recommend 12 things — each backed by an exact quote.
Compiled by rcmnd.app, not by them: the quotes are theirs, and each links to the post it was copied from.
Recent
Recommends The Selfish Gene
So one thing is a selfish gene that's like understand uh it actually changed uh it actually helped me with the question like whether I want to have kids or not.
youtube.com 23 Oct 2025
Liked The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the Internet
An amazing overview to the history of cryptography.
huyenchip.com 27 Dec 2022
Loved Chaos: Making a New Science
I remember being blown away the first time I read the book.
huyenchip.com 27 Dec 2022
Show 9 more
Liked The Making of the Atomic Bomb
A lengthy but fascinating book on the development of nuclear weapons, which has a lot in common with the development of AI today.
huyenchip.com 27 Dec 2022
Liked Just My Type: A Book About Fonts
We know that fonts can influence how we perceive that text. This book explains why and how we can better utilize this power.
huyenchip.com 27 Dec 2022
Recommends The Design of Everyday Things
A must read for anyone who cares about functional design, from the first User Experience Architect at Apple.
huyenchip.com 27 Dec 2022
Liked Uncommon Genius: How Great Ideas are Born
A collection of interviews with 40 recipients of the MacArthur Fellowship (the genius award) gave me a different perspective on the creative process.
huyenchip.com 27 Dec 2022
What others say about Uncommon Genius: How Great Ideas are Born →
Loved How Not to Be Wrong: The Power of Mathematical Thinking
A wonderful, easy-to-read introduction to applied math and probability.
huyenchip.com 27 Dec 2022
What others say about How Not to Be Wrong: The Power of Mathematical Thinking →
Recommends Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
Taleb’s way of writing probably upsets a lot of people, but if you can get over it, it’s definitely worth a read.
huyenchip.com 27 Dec 2022
What others say about Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets →
Liked Fads and Fallacies in the Name of Science
Despite being written 70 years ago, the book is still insanely relevant today.
huyenchip.com 27 Dec 2022
What others say about Fads and Fallacies in the Name of Science →
Liked Profiles of the Future: An Inquiry into the Limits of the Possible
Clarke is best known for his science fiction (2001: A Space Odyssey), but his science nonfiction is fascinating too.
huyenchip.com 27 Dec 2022
What others say about Profiles of the Future: An Inquiry into the Limits of the Possible →
Loved Complex Adaptive Systems: An Introduction to Computational Models of Social Life
I spend a lot of time thinking about modeling computational systems, and this book introduced me to modeling social systems.
huyenchip.com 27 Dec 2022
Where they publish
Blog huyenchip.com Posts on getting machine learning into production. Has a feed.
Long posts working through one system-design question at a time — evaluation, agents, inference cost, what an ML platform is actually made of.
Link verified 20 Sept 2026.
x.com @chipro Posts.
Recent
- Braden and team are great! Highly recommend this program for independent researchers working on cool stuff! 20 Oct 2026
- Interesting approach. Models can't output freeform text but can choose from a set of predefined values. Could be useful for data labeling and tasks with a fixed list of possible actions. Unclear how reasoning would work though, but it's super cheap (output tokens are free!) 15 Sept 2026
- i'm researching best practices for skills. what's the most number of skills you've had installed for your agents? 25 Aug 2026
Show 13 more
- real question: why does gpt 5.6 over engineer so much? 25 Aug 2026
- what's a good model tiering system? i'm sick of telling my agent orchestrator things like: "for Claude, use model X, for OpenAI, use model Y, etc." i want to be able to tell my orchestrator: "use models tier ..." for this kind of task 17 Aug 2026
- that's the problem he should've sent them in all caps 10 Aug 2026
- why don't model providers price tokens like electricity, higher price during peak hours and cheaper off peak? 9 Aug 2026
- Auto science is gonna be 🔥 Congrats anh Quoc and team! 5 Aug 2026
- bored at the airport so i made this 5 Jun 2026
- Congrats to @AlecRad @Luke_Metz and @soumithchintala! It's really cool to see work produced by a group of 20-somethings without a single PhD between them winning this award. I hope to see these three collaborate again one day :) 23 Apr 2026
- How long do you think AI will be able to fully automate your job? 11 Feb 2026
- I built https://t.co/4rAUTXjAhc to help me stay-up-date with new AI stuff. It's tracking 14K open source repos so far, with contributions from over 145K developers. Every day, it: - searches for new AI repos (based on 123 keywords and topics) - surfaces repos that are gaining traction, and - categorizes each repo The annotations are done by AI so they are not super accurate, but they've helped me find some useful stuff. It also lets me see where the contributors are, so when I travel, I can find folks doing cool stuff in a new city or country. 29 Jan 2026
- Super impressed by the projects at the Agentic Hackathon last weekend! Many teams work on really hard/important problems: * Long running tasks: memory management, recovering from mid-task failures, and maintaining consistency across steps and sub-agents * Adaptive retrieval from multiple sources: databases, search indices, and websites * Agents that work with voice, video, and even 3D environments If you are in SF, come check out the finalist demos tomorrow! https://t.co/JytAN5xqyG There will be talks by Douglas Eck, who is doing amazing work with Veo and Imagen and many other awesome folks.… 14 Jan 2026
- OMG there's a bookstore dedicated to technical books in Taipei! 4 Dec 2025
- After years of following @lennysan's wonderful takes on product, I finally had the opportunity to chat with him about AI products! https://t.co/buFxQkTqyH 1. Many AI product problems aren’t because of AI. It’s usually because of user experience, data quality, or organizational structure. A chatbot failed to get traction because their targeted users simply couldn’t type (because their hands were usually busy -- taking care of kids or driving), so showing pre-populated questions and adding a voice option significantly improved traction. Another team told me their lead scoring model was broken.… 30 Oct 2025
- A hiring manager just told me that it's a red flag 🚩 if a software engineering candidate hasn't experimented with vibe coding. Thoughts? 22 Oct 2025
Link verified 20 Sept 2026. Recent items update automatically from the channel.
GitHub @chiphuyen Code, and the notes that became the books.
Recent
- aie-book — [WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025) 3 Jul 2026
- chiphuyen 24 Jun 2026
- dmls-book — Summaries and resources for Designing Machine Learning Systems book (Chip Huyen, O'Reilly 2022) 9 Jun 2026
Show 9 more
- sniffly — Claude Code dashboard with usage stats, error analysis, and sharable feature 8 Aug 2025
- ml-interviews-book — https://huyenchip.com/ml-interviews-book/ 21 Mar 2025
- sotawhat — Returns latest research results by crawling arxiv papers and summarizing abstracts. Helps you stay afloat with so many new papers everyday. 2 Feb 2024
- just-pandas-things — An ongoing list of pandas quirks 8 May 2023
- machine-learning-systems-design — A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book` 15 Apr 2023
- metaflow-transformers-tutorials — Metaflow tutorials for ODSC West 2021 16 Nov 2021
- stanford-tensorflow-tutorials — This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research. 22 Dec 2020
- lazynlp — Library to scrape and clean web pages to create massive datasets. 11 Nov 2020
- python-is-cool — Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more. 27 Dec 2019
Link verified 20 Sept 2026. Recent items update automatically from the channel.
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