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](https://www.union.ai/).Ā
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](https://www.union.ai/) also provides the open-source solutions Pandera for statistical validation and UnionML.
Upcoming events (1)
See all- 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
