Union.ai

True or false: Reproducibility is critical to reliable AI. When you're building Compound AI Systems, consistency across workflows can be the difference between success and failure at scale. In our latest blog, we break down how reproducible workflows enable scalable, production-grade AI—helping teams avoid the pitfalls of ad-hoc experimentation and fragmented pipelines. ?? Key takeaways: ? Why reproducibility is essential for Compound AI Systems ? How to build scalable, version-controlled workflows ? Best practices for AI/ML teams deploying at scale If you're tackling AI at enterprise scale, don’t miss this. ?? ?? Read more: https://lnkd.in/ec9EbmSe #AI #MLOps #MachineLearning #CompoundAI #DataScience

Join this hands-on workshop to learn how to build secure AI pipelines with the Union platform. This workshop will cover: - Overview of common vulnerabilities in ML pipelines - Flyte's OAuth 2.0 implementation and how to use it - From auth to authz: Role Based Access Control in Union You're all welcome to join!

Join this workshop with Niels Bantilan designed to help you get started with Pandera, a powerful Python library for data validation and quality assurance. GitHub Repro to follow along: https://lnkd.in/eN85jcjp Ensuring the integrity and reliability of your data is crucial in the world of data science and machine learning. Pandera makes this process seamless by providing tools to define, validate, and enforce data schemas directly in your workflows. By the end of this workshop, you’ll have the skills to implement robust data validation checks that act like "unit tests" for your data, ensuring cleaner datasets and more trustworthy insights.

??? Union + Together AI: Seamless Contextual RAG Deployment Building advanced AI applications should feel like assembling Lego—fast, intuitive, and modular. That’s exactly what we’ve made possible with our new Union + Together AI integration. Now, you can build and deploy Contextual RAG applications directly from your Project Jupyter notebook using Milvus vector database, without worrying about infrastructure headaches. Just experiment, iterate, and scale—we handle the rest. Check out the details ?? https://hubs.la/Q0384mf90 #AI #MachineLearning #RAG #LLM #TogetherAI #MLOps #GenerativeAI

???Secure Your AI Supply Chain Against Hidden Threats!??? Did you know pickle files and data poisoning could silently compromise your AI systems? ???? Our latest blog + Notebook dives into two critical ML security risks—and shows you how to fight back! ???What You’ll Learn: ? How?pickle-based attacks?execute malicious code in production ? Why?data poisoning?can turn LLMs "toxic" ? Step-by-step mitigations:?secure serialization (ONNX),?data validation (Pandera), and?defense-in-depth?with Union’s architecture ????Key Tools: Replace risky pickle files with?secure formats like ONNX Detect poisoned data using?Pandera schemas Leverage Union’s?container isolation?and?immutable storage ???Why It Matters: Security isn’t an afterthought—it’s the foundation of trustworthy AI. ???Read Now?→ https://hubs.la/Q037_Ylv0 ?? Join this week's live workshop to learn how to get started: https://lnkd.in/e2U7hMcC #MachineLearning #CyberSecurity #MLOps #AI #DataScience #DevSecOps

??? This workshop will equip you with the skills to effectively build your ML pipelines and reliable AI workflows using Python, scikit-learn and Union. We'll also see how to serve your own ML model! Workshop links to follow along: https://signup.union.ai/ (can take 3-5 min to process) https://lnkd.in/gvZHSYab Join the Slack to stay updated with the community https://slack.flyte.org/

Join this workshop with Niels Bantilan designed to help you get started with Pandera, a powerful Python library for data validation and quality assurance! ?? Ensuring the integrity and reliability of your data is crucial in the world of data science and machine learning. Pandera makes this process seamless by providing tools to define, validate, and enforce data schemas directly in your workflows. By the end of this workshop, you’ll have the skills to implement robust data validation checks that act like "unit tests" for your data, ensuring cleaner datasets and more trustworthy insights. https://lnkd.in/gj2Dq2x9

This workshop with Sage Elliott will equip you with the skills to harness these benefits effectively and show you how to build AI workflows for fine tuning LLMs using Hugging Face, and Union.ai. Workshop links to follow along: https://signup.union.ai/ (can take 3-5 min to process) https://lnkd.in/g7wfq3KY Ask questions after the workshop in the Slack community https://slack.flyte.org/ Next Live Event: Intro to ML Pipelines: Build Reliable AI Workflows https://lnkd.in/gfhHh7D8

??? Join us for a hands-on workshop where you’ll learn how to fine-tune LLMs using Hugging Face and Union.ai to create scalable, reproducible AI pipelines. ?? What You'll Learn: MLOps / LLMOps pipeline fundamentals Fine-tune an LLM for text classification on unstructured data Build scalable workflows with Union Deploy & interact with your fine-tuned model Concepts learned in this workshop scale to more complex workflows and LLMs.

This workshop will equip you with the skills to effectively build your ML pipelines and reliable AI workflows using Python, scikit-learn and Union. Workshop links to follow along: https://signup.union.ai/ (can take 3-5 min to process) https://lnkd.in/gvZHSYab https://slack.flyte.org/ This workshop will equip you with the skills to effectively build your ML pipelines and reliable AI workflows using Python, scikit-learn and Union. What you'll learn can be transferred to more complex AI pipelines and machine learning libraries. The Modern MLOps tooling will provide a reliable framework for your machine learning operations by streamlining processes, increasing efficiency, and adding reproducibility to your AI applications.