ZenML provides a framework for building infrastructure-agnostic machine learning pipelines.

ZenML

ZenML | 7,078 followers on LinkedIn. #Opensource #MLOps #Framework that integrates all your ML tools.
Run ML pipelines on any stack with minimum effort! 
 | ZenML is an extensible, open-source MLOps framework for creating portable, production-ready MLOps pipelines. It&#39;s built for data scientists, ML Engineers, and MLOps Developers to collaborate as they develop to production. ZenML has simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows.

Scott Sage

Senior Product Designer @ ZenML

Zuri Negrín

CEO @ ZenML ⛩️ open-source MLOps Framework

Adam Probst

Information Security Officer @ ZenML

Stefan Nica

⛩️ Run your first #MLOps pipeline in just 11 minutes! 🧘🏽♀️ In this video, Hamza provides an in-depth step-by-step guide on creating your first MLOps pipeline and demonstrates how ZenML seamlessly integrates with your favorite tools and existing infrastructure. It also shows the ease of transitioning from local debugging to cloud production and how to maintain visibility and control over various models and data. 📊 With ZenML, you can easily convert legacy code into a pipeline, automate versioning of data sets, scale up to the cloud for more resources, and even deploy models with a single click.👌 -> Watch it here on youtube: https://lnkd.in/dADeBehJ -> And try it yourself: https://www.zenml.io/ #opensource

How to deploy RAG in production for the enterprise

Congratulations to the lucky winners of our LLM Engineer's Handbook challenge!

Read more about modern MLOps experiment tracking in 2024

Book giveaway starts today -> Write your biggest challenges in LLM Engineering in the comments on Hamza's post!

Our LLM Engineer's Handbook repository made ~900 🌟 in less than a week, already in Packt 's top 15 most popular repositories. I put tons of effort into this repository trying to show the best practices for how to: - structure a professional repository - use the clean architecture paradigm to define the flow between classes and functions - build a modular LLM system using the FTI architecture - use MLOps & LLMOps best practices by leveraging ZenML , Hugging Face and Comet - use Amazon Web Services (AWS) SageMaker to deploy training, evaluation jobs and inference real-time services programmatically - deploy the whole LLM system to AWS using Docker, Inc and ZenML - build a CI/CD/CT pipeline using GitHub Actions and ZenML You can play around with the repository for free, as everything is open-source. If you are beginning your journey in AI, SWE, LLMs, and MLOps, consider buying our book, which will explain in the smallest detail what is happening in the repository. Let's make this repository the number 1 in Packt's organization! Still, 2k 🌟 to go 😂 📙 Amazon (30% off promotion): https://lnkd.in/dVgFJtzF 💻 GitHub: https://lnkd.in/dcp9yEyX #artificialintelligence #machinelearning #generativeai #mlops . 💡 Follow me for daily content on production ML and MLOps engineering.

Paul Iusztin

So excited! My LLM Engineer's Handbook is finally live and ready to provide a framework for building LLM and RAG apps! The journey of writing the book was hardcore. ~ About 4 months ago, Maxime Labonne and I started working on the book. We had to develop code based on constantly shifting AI trends and write 400 pages at an alarming pace. I probably wouldn't have made it if I hadn't quit my job, and I don't know how Maxime did. Still, on this tight deadline, we managed to deliver quality and something unique: "𝘈 𝘣𝘰𝘰𝘬 𝘵𝘩𝘢𝘵 𝘵𝘦𝘢𝘤𝘩𝘦𝘴 𝘺𝘰𝘶 𝘢 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘢𝘯𝘥 𝘮𝘪𝘯𝘥𝘴𝘦𝘵 𝘰𝘯 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯-𝘭𝘦𝘷𝘦𝘭 𝘓𝘓𝘔 𝘢𝘯𝘥 𝘙𝘈𝘎 𝘢𝘱𝘱." We haven't focused on theory or small projects, as there are other excellent books on the topic. But we wanted to provide people with an end-to-end experience in building a #GenAI product. The book will fill in the gaps, showing you how the worlds of DE, SWE, AI, and MLOps work together and highlighting how to solve problems with LLMs and RAG. 𝗧𝗵𝗲 𝗯𝗼𝗼𝗸 𝗶𝘀 𝘀𝗽𝗹𝗶𝘁 𝗶𝗻𝘁𝗼 𝟯 𝗺𝗮𝗶𝗻 𝘀𝗲𝗰𝘁𝗶𝗼𝗻𝘀: 1. 𝘋𝘢𝘵𝘢 - Collect and clean custom data - Feature engineering - Creating custom datasets for fine-tuning LLMs - Populating a Qdrant vector DB for RAG 2. 𝘎𝘦𝘯𝘈𝘐 & 𝘓𝘓𝘔𝘴: - Fine-tuning and optimizing LLMs using Unsloth AI - Advanced RAG 3. 𝘚𝘺𝘴𝘵𝘦𝘮𝘴 & 𝘔𝘓𝘖𝘱𝘴: - Design an LLM system using MLOps & LLMOps best practices - Deploying the LLM system to AWS SageMaker - Orchestrate the ML pipelines using ZenML - Prompt monitoring using Opik from Comet ...while everything is connected into a single production-ready project, teaching you how to build your LLM twin. Also, the diagrams will be in color... and there are many! 🔗 𝘊𝘰𝘯𝘴𝘪𝘥𝘦𝘳 𝘴𝘶𝘱𝘱𝘰𝘳𝘵𝘪𝘯𝘨 𝘰𝘶𝘳 𝘸𝘰𝘳𝘬 𝘣𝘺 𝘣𝘶𝘺𝘪𝘯𝘨 𝘵𝘩𝘦 𝘣𝘰𝘰𝘬 𝘢𝘵: https://lnkd.in/dVgFJtzF I'm incredibly grateful to Julien Chaumond (CTO of Hugging Face ) and Hamza Tahir (CTO of ZenML) for their fantastic endorsements on our book! Also, I'm super happy to write the book with Maxime (the ninja of fine-tuning LLMs) and the Packt team, especially with Gebin George , for coordinating the whole thing. I hope we can do this again in the future! 🔗 Amazon: https://lnkd.in/dVgFJtzF 🔗 GitHub: https://lnkd.in/dcp9yEyX #artificialintelligence #machinelearning #generativeai #mlops

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