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
ā¢OnlineAI Book Club: Deep Learning for Biology
OnlineNovember's book is " Deep Learning for Biology"!
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: Deep Learning for Biology
Authors: Charles Ravarani, Natasha Latysheva
Published: July 2025https://learning.oreilly.com/library/view/deep-learning-for/9781098168025/
Chapters:
1. Introduction
2. Learning the Language of Proteins
3. Learning the Logic of DNA
4. Understanding DrugāDrug Interactions Using Graphs
5. Detecting Skin Cancer in Medical Images
6. Learning Spatial Organization Patterns Within Cells
7. Tips and Tricks for Deep Learning in BiologyBook Description
Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems.
Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data.- Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection
- Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders
- Use Python and interactive notebooks for hands-on learning
- Build problem-solving intuition that generalizes beyond biology
Whether youāre exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.
https://learning.oreilly.com/library/view/deep-learning-for/9781098168025/
2 attendees
Past events
101
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