About us
Welcome to the Building AI Together meetup!
đŹ Join the community Slack group: https://slack.flyte.org/
Our community meetups are for data scientists and engineers in machine learning, infrastructure, and data. Our central topics are:
- best practices for putting ml in production
- ml and data workflow automation
- machine learning at scale
- data and machine learning pipelines
- distributed computing
- Kubernetes-native machine learning and data workflows
- MLOps
This group is run by the wonderful people at [Union.ai](https://www.union.ai/).Â
The founding team at Union created Flyte, the data-ware machine learning orchestrator.
Check Flyte out on GitHub â: https://github.com/flyteorg/flyte
Flyte is a Kubernetes-native open-source platform for production-grade data and machine-learning pipelines. It caches executions, tracks data and dependencies, and integrates with countless data and ML stacks, including AWS Sagemaker, Distributed Tensorflow, PyTorch Distributed, Ray, AWS Batch, Kubernetes Pods, and more.
[Union.ai](https://www.union.ai/) also provides the open-source solutions Pandera for statistical validation and UnionML.
Upcoming events
1
AI Book Club: Context Engineering for Multi-Agent Systems
¡OnlineOnlineMarch's book is "Context Engineering for Multi-Agent Systems"!
This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.
Feel free to join the discussion even if you have not read the book chapters! :)
Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/
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About the book:
Title: Context Engineering for Multi-Agent Systems
Authors: Denis Rothman
Published: November 2025https://learning.oreilly.com/library/view/context-engineering-for/9781806690053/
Chapters:
Chapter 1: From Prompts to Context: Building the Semantic Blueprint
queue
Chapter 2: Building a Multi-Agent System with MCP
Chapter 3: Building the Context-Aware Multi-Agent System
Chapter 4: Assembling the Context Engine
Chapter 5: Hardening the Context Engine
Chapter 6: Building the Summarizer Agent for Context Reduction
Chapter 7: High-Fidelity RAG and Defense: The NASA-Inspired Research Assistant
Chapter 8: Architecting for Reality: Moderation, Latency, and Policy-Driven AI
Chapter 9: Architecting for Brand and Agility: The Strategic Marketing Engine
Chapter 10: The Blueprint for Production-Ready AI
Chapter 11: Unlock Your Exclusive Benefits####
Book Description
Generative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system youâll learn to design and apply across real-world scenarios.
Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, youâll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol. As the engine evolves, youâll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. Youâll also harden the system into a resilient architecture, then see it pivot across domains, from legal compliance to strategic marketing, proving its domain independence.By the end of this book, youâll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence.
https://learning.oreilly.com/library/view/context-engineering-for/9781806690053/
6 attendees
Past events
108
