DAGWorks Inc.

Happy Thursday here's the TL;DR: #Hamilton highlights: - new feature - @datasaver & @dataloader decorators - new tutorial videos on Hamilton & using the VSCode Plugin - reminder: Hamilton OS Meetup Group meeting next month (link in post to sign up) #Burr highlights: - Burr is making it to production; we're getting feature requests - add yours! - minor 🐛 fixes In the wild: - Hamilton making it on the Kedro blog, - ChatGPT knows about Hamilton, - Hamilton on Reddit, Inc. SEO

🌟5 Key Takeaways for Successful Product Management | Build, Empathy, and Strategic Choices 🌟 🔊 Featuring: Stefan Krawczyk: CEO & Co-founder, DAGWorks Inc. 🎓 Talk Title: Getting Higher ROI on MLOps Initiatives 🌟 More about the Talk: MLOps is hard because there are so many “things” that you might want to integrate and connect with: -A/B testing, -Feature stores, -Model registries, -Data catalogs, -Lineage systems, -Python dependencies, -Machine learning libraries, -LLM APIs, -Orchestration systems, -Online vs offline systems, -Speculative business ideas, etc. In this talk, Stefan covers five lessons that he learned while building out the self-service MLOps platform for over 100 data scientists at Stitch Fix. This talk is for anyone building their own or buying it all off the shelf. 📺 Watch the Full Video: Learn what to do and what to avoid so you can increase your ROI on MLOps initiative over here https://lnkd.in/eB7x9WK3

🌟5 Key Takeaways for Successful Product Management | Build, Empathy, and Strategic Choices 🌟 🔊 Featuring: Stefan Krawczyk: CEO & Co-founder, DAGWorks Inc. MLOps is hard because there are so many “things” that you might want to integrate and connect with: - A/B testing, Feature stores, Model registries, Python dependencies, Machine learning libraries, LLM APIs, Orchestration systems and more Stefan covers five lessons he learned while building the self-service MLOps platform for over 100 data scientists. This talk is for anyone building their own or buying it all off the shelf. 📺 Watch the Full Video: https://lnkd.in/d369g2Z8 #mlops #llms

Happy Friday! After taking a break, I'm back! Here's the TL;DR: more in the newsletter! #Hamilton release highlights: - User contributed Graceful Failure Adapter improvements, - #SparkConnect support - #Python Async updates & upgrades, - new Schema view in the Hamilton UI Reminder: Hamilton OS Meetup Group is scheduled for August #Burr release highlights: - new GraphBuilder API to simplify code, - serde specification for test case creation, - new examples: adaptive CRAG & FastAPI async streaming example with Server-Sent-Events React . Seen in the wild: - a Hamilton user blog.

Last Night, Building AI Agents teamed up with MLOps Community to host a Demo Day at Microsoft Reactor in San Francisco. We had a great turnout and some awesome tool demos. It's always exciting to see what the community is working on, we can't wait to see where these products go! We hope you had a good time, learned a thing or two, and forged some new connections in the Building AI Agents space! Charles Frye of Modal kicked off the night by showcasing their suite of cloud tools for building and hosting AI applications. Make sure to check out their website if you need to train and deploy AI models for your application. Thank you to the founders who presented, we couldn't do these events without you. Anastasiia Biloshytska - Murnitur Simon Farshid - assistant-ui Haig Douzdjian - Manot Alex Reibman - AgentOps.ai Stefan Krawczyk - Burr Marko O. Kraemer - Softgen Special thanks to Modal for graciously sponsoring this event, and to Microsoft Reactor for the space. Rahul Parundekar Shreeganesh R. Hari Masoor Ravi Ramachandran

Check out DAGWorks Inc. latest blog post about how Wren AI is leveraging Hamilton to scale our LLM pipeline with the latest feature updates, nicely written by Elijah ben Izzy! With Hamilton, it easily standardizes how we build and scale our pipelines with async way in Python. Check it out here: https://lnkd.in/ghcGTsPb

Wrote about streaming with server-sent-events using FastAPI, #Burr, and a simple @React/#tailwind app. Super excited about this -- I only found a few good resources on streaming text back to the user, but it feels like table-stakes these days. Hoping this helps people get started! https://lnkd.in/gUvxmfS4

🤔 Is #langchain usable? 🤔 I've been thinking more and more about this reddit comment from a bit ago. To be honest, this debate is a little tired (and, quite well-answered here), and Langchain takes *way* too much flack IMO. That said, I think there's a one-level-deeper conversation to be had that's as old as software engineering itself (OK, not that old): - What makes a good abstraction? - When should you reach for a framework/build something yourself? While the solution to this is entirely use-case dependent, common mistakes tend to fit in different buckets. I've been thinking of writing about this (tossing around the term "framework-brain") -- would love your ideas! Good/bad experiences adopting abstractions? Pain of production versus ease of initial prototyping? Would love your thoughts! I'm really excited about #Burr and #Hamilton as I think they hit the nail on the head with the right abstraction for a large number of use-cases. That said, building using #AI is still in its infancy, and we have a lot to learn!

Happy Thursday. Here's the news from us: 🌟 Hamilton Release highlights: > schema tracking & validation. Heard of data contracts? Here's a lightweight way to get similar functionality -- by tracking schemas. ⭐️ Burr Release highlights: > Jupyter auto-renders the application graph. > Compare view in the Burr UI. 📰 Blog post: GraphRAG with #Hamilton, #Burr, & FalkorDB. Link in newsletter. 🤝 Hamilton OS Meetup Group: June recording & slides are up. August scheduled. Link in the newsletter. Have a great week!

🌟 Get Ready to Supercharge Your Software Development! 🌟 The countdown is on! 🚀 Join us on June 26 at 9 AM MST for an exclusive virtual event featuring Stefan Krawczyk and Elijah ben Izzy. This is your chance to learn from two of the industry's top experts in software development, data science, and machine learning. 📅 Event Details: Date: June 26 Time: 9 AM MST Registration Link: https://lnkd.in/gaC9rNny Why You Should Attend: 🔹Scalability Techniques: Master the art of structuring your code to support scalable projects using Hamilton’s DAGs. 🔹Enhanced Collaboration: Uncover strategies that foster better teamwork and communication within your engineering team. 🔹Efficient Debugging and Reviews: Learn how to set up a coding environment that simplifies debugging and accelerates code reviews. 🔹Real-world Applications: Hear about practical case studies from leading companies like Stitch Fix and British Cycling. Meet Our Speakers: 🗣️ Stefan Krawczyk: CEO of DAGWorks Inc., a seasoned Silicon Valley leader with over 15 years of experience in data science and machine learning. Creator of the open-source frameworks Hamilton and Burr. 🗣️ Elijah Ben Izzy: CTO of DAGWorks Inc., an expert in building sophisticated data pipelines and enhancing the productivity of data scientists. Don’t miss out on this opportunity to gain valuable insights and elevate your software development practices. Register now and be part of this transformative session! 🔗 Register for the Event: https://lnkd.in/gaC9rNny #SoftwareDevelopment #EngineeringManagement #DataScience #MachineLearning #Webinar #OpenSource #TechLeadership OpenTeams