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The first episode of The Pragmatic Engineer Podcast is out. Expect similar episodes every other Wednesday. You can add the podcast in your favorite podcast player, and have future episodes downloaded automatically. Listen now on Apple, Spotify, and YouTube. Brought to you by: • Codeium: ​​Join the 700K+ developers using the IT-approved AI-powered code assistant. • TLDR: Keep up with tech in 5 minutes — On the first episode of the Pragmatic Engineer Podcast, I am joined by Simon Willison. Simon is one of the best-known software engineers experimenting with LLMs to boost his own productivity: he’s been doing this for more than three years, blogging about it in the open. Simon is the creator of Datasette, an open-source tool for exploring and publishing data. He works full-time developing open-source tools for data journalism, centered on Datasette and SQLite. Previously, he was an engineering director at Eventbrite, joining through the acquisition of Lanyrd, a Y Combinator startup he co-founded in 2010. Simon is also a co-creator of the Django Web Framework. He has been blogging about web development since the early 2000s. In today’s conversation, we dive deep into the realm of Gen AI and talk about the following:  • Simon’s initial experiments with LLMs and coding tools • Why fine-tuning is generally a waste of time—and when it’s not • RAG: an overview • Interacting with GPTs voice mode • Simon’s day-to-day LLM stack • Common misconceptions about LLMs and ethical gray areas  • How Simon’s productivity has increased and his generally optimistic view on these tools • Tips, tricks, and hacks for interacting with GenAI tools • And more! I hope you enjoy this episode. — In this episode, we cover: (02:15) Welcome (05:28) Simon’s ‘scary’ experience with ChatGPT (10:58) Simon’s initial experiments with LLMs and coding tools (12:21) The languages that LLMs excel at (14:50) To start LLMs by understanding the theory, or by playing around? (16:35) Fine-tuning: what it is, and why it’s mostly a waste of time (18:03) Where fine-tuning works (18:31) RAG: an explanation (21:34) The expense of running testing on AI (23:15) Simon’s current AI stack  (29:55) Common misconceptions about using LLM tools (30:09) Simon’s stack – continued  (32:51) Learnings from running local models (33:56) The impact of Firebug and the introduction of open-source  (39:42) How Simon’s productivity has increased using LLM tools (41:55) Why most people should limit themselves to 3-4 programming languages (45:18) Addressing ethical issues and resistance to using generative AI (49:11) Are LLMs are plateauing? Is AGI overhyped? (55:45) Coding vs. professional coding, looking ahead (57:27) The importance of systems thinking for software engineers  (1:01:00) Simon’s advice for experienced engineers (1:06:29) Rapid-fire questions — Where to find Simon Willison: • X: https://x.com/simonw • LinkedIn: https://www.linkedin.com/in/simonwillison/ • Website: https://simonwillison.net/ • Mastodon: https://fedi.simonwillison.net/@simon — Referenced: • Simon’s LLM project: https://github.com/simonw/llm • Jeremy Howard’s Fast Ai: https://www.fast.ai/ • jq programming language: https://en.wikipedia.org/wiki/Jq_(programming_language) • Datasette: https://datasette.io/ • GPT Code Interpreter: https://platform.openai.com/docs/assistants/tools/code-interpreter • Open Ai Playground: https://platform.openai.com/playground/chat • Advent of Code: https://adventofcode.com/ • Rust programming language: https://www.rust-lang.org/ • Applied AI Software Engineering: RAG: https://newsletter.pragmaticengineer.com/p/rag • Claude: https://claude.ai/ • Claude 3.5 sonnet: https://www.anthropic.com/news/claude-3-5-sonnet • ChatGPT can now see, hear, and speak: https://openai.com/index/chatgpt-can-now-see-hear-and-speak/ • GitHub Copilot: https://github.com/features/copilot • What are Artifacts and how do I use them?: https://support.anthropic.com/en/articles/9487310-what-are-artifacts-and-how-do-i-use-them • Large Language Models on the command line: https://simonwillison.net/2024/Jun/17/cli-language-models/ • Llama: https://www.llama.com/ • MLC chat on the app store: https://apps.apple.com/us/app/mlc-chat/id6448482937 • Firebug: https://en.wikipedia.org/wiki/Firebug_(software)# • NPM: https://www.npmjs.com/ • Django: https://www.djangoproject.com/ • Sourceforge: https://sourceforge.net/ • CPAN: https://www.cpan.org/ • OOP: https://en.wikipedia.org/wiki/Object-oriented_programming • Prolog: https://en.wikipedia.org/wiki/Prolog • SML: https://en.wikipedia.org/wiki/Standard_ML • Stabile Diffusion: https://stability.ai/ • Chain of thought prompting: https://www.promptingguide.ai/techniques/cot • Cognition AI: https://www.cognition.ai/ • In the Race to Artificial General Intelligence, Where’s the Finish Line?: https://www.scientificamerican.com/article/what-does-artificial-general-intelligence-actually-mean/ • Black swan theory: https://en.wikipedia.org/wiki/Black_swan_theory • Copilot workspace: https://githubnext.com/projects/copilot-workspace • Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems: https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321 • Bluesky Global: https://www.blueskyglobal.org/ • The Atrocity Archives (Laundry Files #1): https://www.amazon.com/Atrocity-Archives-Laundry-Files/dp/0441013651 • Rivers of London: https://www.amazon.com/Rivers-London-Ben-Aaronovitch/dp/1625676158/ • Vanilla JavaScript: http://vanilla-js.com/ • jQuery: https://jquery.com/ • Fly.io: https://fly.io/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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Hallo everyone, another week another session!

Perhaps the hardest part about CUDA is getting started. How do you set up your environment? What exactly is a "kernel" and how is it used? How do you actually write a kernel and call it from Python? How do you test and debug your code? In this talk Jeremy will show how to make the simple stuff easy, so then you can focus on the hard stuff!

RSVP: https://discord.gg/Kvr5pmHXpW?event=1198706289858068481

Suite à une présentation de Jeremy Howard (co-fondateur de fast.ai) sur l'exploitation des modèles de language faite ce mois-ci (Septembre 2023), j'aimerais vous la présenter en Français et voir ensemble les sujets présentés tels que

• Introduction et idées de base des modèles de langage
• Limites et capacités de GPT-4
• Applications d'IA dans l'écriture de code, l'analyse de données et l'OCR
• Conseils pratiques sur l'utilisation de l'API OpenAI
• et en bref quelques conseils d'experts sur des sujets techniques tels que le réglage fin (fine-tuning), le décodage de jetons et l'exécution d'instances privées de modèles GPT.
• ... et plus

Je parlerai aussi du dernier venu, le modèle de langage Mistral, développé par une startup française Mistral.AI et disponible sur le HuggingFace hub. L'actualité LLM est toujours aussi rapide et fascinante !

Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the Technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the Book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's Inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the Reader You’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the Authors Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science. Quotes Full of useful shared experience and practical advice. Highly recommended. - From the Foreword by Jeremy Howard and Rachel Thomas Great examples and an informative walk-through of the data science process. - David Meza, NASA Offers interesting perspectives that cover many aspects of practical data science; a good reference. - Pascal Barbedor, BL SET R you ready to get data science done the right way? - Taylor Dolezal, Disney Studios

Summary Machine learning is a class of technologies that promise to revolutionize business. Unfortunately, it can be difficult to identify and execute on ways that it can be used in large companies. Kevin Dewalt founded Prolego to help Fortune 500 companies build, launch, and maintain their first machine learning projects so that they can remain competitive in our landscape of constant change. In this episode he discusses why machine learning projects require a new set of capabilities, how to build a team from internal and external candidates, and how an example project progressed through each phase of maturity. This was a great conversation for anyone who wants to understand the benefits and tradeoffs of machine learning for their own projects and how to put it into practice.

Introduction

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Kevin Dewalt about his experiences at Prolego, building machine learning projects for Fortune 500 companies

Interview

Introduction How did you get involved in the area of data management? For the benefit of software engineers and team leaders who are new to machine learning, can you briefly describe what machine learning is and why is it relevant to them? What is your primary mission at Prolego and how did you identify, execute on, and establish a presence in your particular market?

How much of your sales process is spent on educating your clients about what AI or ML are and the benefits that these technologies can provide?

What have you found to be the technical skills and capacity necessary for being successful in building and deploying a machine learning project?

When engaging with a client, what have you found to be the most common areas of technical capacity or knowledge that are needed?

Everyone talks about a talent shortage in machine learning. Can you suggest a recruiting or skills development process for companies which need to build out their data engineering practice? What challenges will teams typically encounter when creating an efficient working relationship between data scientists and data engineers? Can you briefly describe a successful project of developing a first ML model and putting it into production?

What is the breakdown of how much time was spent on different activities such as data wrangling, model development, and data engineering pipeline development? When releasing to production, can you share the types of metrics that you track to ensure the health and proper functioning of the models? What does a deployable artifact for a machine learning/deep learning application look like?

What basic technology stack is necessary for putting the first ML models into production?

How does the build vs. buy debate break down in this space and what products do you typically recommend to your clients?

What are the major risks associated with deploying ML models and how can a team mitigate them? Suppose a software engineer wants to break into ML. What data engineering skills would you suggest they learn? How should they position themselves for the right opportunity?

Contact Info

Email: Kevin Dewalt [email protected] and Russ Rands [email protected] Connect on LinkedIn: Kevin Dewalt and Russ Rands Twitter: @kevindewalt

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Prolego Download our book: Become an AI Company in 90 Days Google Rules Of ML AI Winter Machine Learning Supervised Learning O’Reilly Strata Conference GE Rebranding Commercials Jez Humble: Stop Hiring Devops Experts (And Start Growing Them) SQL ORM Django RoR Tensorflow PyTorch Keras Data Engineering Podcast Episode About Data Teams DevOps For Data Teams – DevOps Days Boston Presentation by Tobias Jupyter Notebook Data Engineering Podcast: Notebooks at Netflix Pandas

Podcast Interview

Joel Grus

JupyterCon Presentation Data Science From Scratch

Expensify Airflow

James Meickle Interview

Git Jenkins Continuous Integration Practical Deep Learning For Coders Course by Jeremy Howard Data Carpentry

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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