What weāre about
Welcome to the Building AI Together meetup!
š¬ 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.
The founding team at Union created Flyte, the data-ware machine learning orchestrator.
Check Flyte out on GitHub ā: 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 also provides the open-source solutions Pandera for statistical validation and UnionML.
Upcoming events (2)
See all- Scaling AI pipelines to the next level with Flyte & UnionLink visible for attendees
REGISTER HERE TO GET THE EVENT LINK AND RECORDING:
https://www.eventbrite.com/e/scaling-ai-pipelines-to-the-next-level-with-flyte-union-tickets-1323728620939### What youāll learn & do:
Scale Workflows Effortlessly
Discover how to expand compute clusters across clouds and reuse containerized tasks to slash latency and resource waste. Live Demo: Configure multi-cluster Flyte deployments and workload placement to balance cost, performance, and reliability.
Master Parallelism with MapTasks (and Beyond)
Learn when to use Flyteās MapTasks for high-fan-out workflows. Weāll explore their power (low overhead, programmatic iteration) and their limits (Kubernetes metadata bottlenecks).
Live demo: Optimize a real-world workflow using MapTasks, then push it to its breaking point to see where Kubernetes struggles.
Break Through Kubernetes Limits with Union
Go beyond Flyteās native capabilities with Union, a control plane that eliminates Kubernetes bottlenecks. See how it:
Auto-offloads metadata to blob storage when K8s etcd is overwhelmed.
Orchestrates multi-cloud clusters seamlessly, with execution placement you control.
Introduces Actors: reusable, hot containers that bypass Pod startup delays for rapid iteration.
Deep Dive: Refactor a failing MapTask into a Union-powered workflow using Actors and cross-cluster mapping.### About Union.ai
Union is the developer suite powering compound AI systems. We make building AI
fast, easy, and scalable.### Stay Connected
š¬ Join our AI & MLOps Slack: https://slack.flyte.org/
ā Explore Flyte on GitHub: https://github.com/flyteorg/flyte
š Learn more: https://union.ai/ - AI Book Club: Hands-On APIs for AI and Data ScienceLink visible for attendees
May's book is "Hands-On APIs for AI and Data Science"!
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: Hands-On APIs for AI and Data Science
Authors: Ryan Day
Published: March 2025
Hands-On APIs for AI and Data ScienceChapters:
- I. Building APIs for Data Science
1. Creating APIs That Data Scientists Will Love
2. Selecting Your API Architecture
3. Creating Your Database
4. Developing the FastAPI Code
5. Documenting Your API
6. Deploying Your API to the Cloud
7. Batteries Included: Creating a Python SDK
II. Using APIs in Your Data Science Project
8. What Data Scientists Should Know About APIs
9. Using APIs for Data Analytics
10. Using APIs in Data Pipelines
11. Using APIs in Streamlit Data Apps
III. Using APIs with Artificial Intelligence
12. Using APIs with Artificial Intelligence
13. Deploying a Machine Learning API
14. Using APIs with LangChain
15. Using ChatGPT to Call Your API
Book Description:
Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects. API skills have become essential for AI and data science success, because they are used in a variety of ways in these fields. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit.
As you complete the chapters in the book, you'll be creating portfolio projects that teach you how to:- Design APIs that data scientists and AIs love
- Develop APIs using Python and FastAPI
- Deploy APIs using multiple cloud providers
- Create data science projects such as visualizations and models using APIs as a data source
- Access APIs using generative AI and LLMs
Learn more about the book here:
https://learning.oreilly.com/library/view/hands-on-apis-for/9781098164409/ - I. Building APIs for Data Science
