It’s amazing to see the impact of open-source projects! Text2Bricks from our ML team was one of many projects made possible by the Open-Sora team at HPC-AI Tech: https://hubs.ly/Q02DMGyW0 More on Lambda’s Text2Bricks model: https://hubs.ly/Q02DMWFL0
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Learn how to get started with LLMs and GenAI with my latest DataBites issue on this topic 👇🏻 I'd love to hear your thoughts! 👀 https://lnkd.in/dAdVndYY
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Data Scientist | Data Engineer | Product Manager | Solving World's Toughest Data+AI Problems @Databricks | Ex-Rubikon Labs | Ex-Emids Technologies
OLMo, powered by Databricks, has already made building and fine-tuning large models as straightforward as possible, and we want to discuss it!🗣️ Join us for a free webinar on March 26 at 9AM PT ⬇️
[Webinar + Q&A] OLMo: Open Language Model
docs.google.com
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🤖 How do we build AI agents? Well, you start by prompting an LLM and then.. you build the rest of the owl. Daniel U. and I wrote a short intro of the rest of our owl, also known as the Gradient Labs backend platform. Link in the comments 💬 (shout out to Encore, Temporal Technologies, incident.io and more: we're standing on the shoulders of giants!)
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Here's my notes and video for the keynote I gave at PyCon Italia ❤️ 🐍 🇮🇹 yesterday, "Closer to the Metal." The post itself is titled "don't worry about LLMs" and that's really what I was trying to get at with this talk. LLMs are a big topic and there is a lot of demand to build them quickly, but the space is very confusing and inflated with hype. How do we remove the hype and use LLMs to build things we need? We focus our attention on one thing at a time and use the practical engineering and machine learning skills we already have.
Don't worry about LLMs
vickiboykis.com
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VP, Data Scientist @ Truist | Physicist | MBA | MSc Physics | Data Science, ML and AI | Computer Vision | ex-IBM | IITB
🎉 Keynote by Vicki Boykis at PyCon Italia 2024. If you haven’t read other blog posts by Vicki, I would urge you to do so. You will certainly find a lot of technical topics explained in quite detail in her blog posts. #python #pycon
Here's my notes and video for the keynote I gave at PyCon Italia ❤️ 🐍 🇮🇹 yesterday, "Closer to the Metal." The post itself is titled "don't worry about LLMs" and that's really what I was trying to get at with this talk. LLMs are a big topic and there is a lot of demand to build them quickly, but the space is very confusing and inflated with hype. How do we remove the hype and use LLMs to build things we need? We focus our attention on one thing at a time and use the practical engineering and machine learning skills we already have.
Don't worry about LLMs
vickiboykis.com
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Really interesting conversation regarding different aspects of machine learning at scale between Nieves Ábalos Serrano (Fonos) and Roberto Sanchis Ojeda (Spotify), covering everything from use cases and models to product testing and team structure.
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The topic of using graph databases and LLMops keeps coming up in conversations, so I'm very excited to have Jason Koo from Neo4j on our next Union.ai fireside chat to talk about it! 🔥 Join the event to learn about advanced RAG techniques with graph databases for LLMs. https://lnkd.in/gzQpYcdi
Advanced RAG techniques with Graph Databases for LLMs | Jason Koo - Neo4j
eventbrite.com
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Basic seat-based pricing will not last in the age of AI! Successful AI-native companies are rolling out hybrid pricing and packaging using different models for each product, for each plan, and for each customer segment. In a recent workshop with SaaStr, Sandhya Hegde broke down the pricing strategy behind products from OpenAI, Midjourney, Character.AI, Writer, and more! Check out the entire recording here: https://lnkd.in/efpC-VZM
Every software company is trying to figure out the best way to price their AI products, services and add on features today and it is ... hard. Shared my lessons and observations from >50 case studies and interviews with AI founders in a workshop with SaaStr today morning! Thank you for building such a great community Jason M. Lemkin and Danielle, Taylor et al for all your feedback on the slide deck! Video in comments if you want to watch/GPT-summarize ;)
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📣 Our very own Joel Alexander spoke at Data Council'24 on "How developer tools are about building repeatable workflows" The talk provides a live demo improving a RAG application: 📈 Use evals on RAG task ⛔ Create custom dataset from eval logs 💸 Improve eval score and save cost using Anthropic Haiku
Last month, at Data Council '24, I committed the cardinal sin of a "Live Demo." Luckily, it paid off. During the AI Launchpad, I previewed what I think is the answer to the number one question we get at Parea AI (YC S23). "How do I structure my LLM dev cycle for speed and quality?" My talk covered: - RAG Observability - Evaluation & Experimentation - Prompt Eng + Deployments Big thanks to Pete Soderling, Zero Prime Ventures & Data Council for hosting a great conference! YouTube link👇🏾
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Everyone in IT has heard of AIOps by now. You may even be using products or features called AIOps. But what is AIOps, really? With input from industry experts — both analysts and vendors — this 10-part blog series will try to answer this question.
Discovering AIOps - Part 1
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