You must register on Partiful to attend: https://partiful.com/e/mvz6EiP3j9pdojKtDr6Y

Join us during SF Tech Week for the exclusive networking event and panel discussion for engineers building AI, ML, and agents. We’ll discuss the most current models, MLOps, infrastructure, and engineering challenges of putting AI into production.

Our panel discussion will feature industry leaders, including Ketan Umare, founder of [Union.ai](http://union.ai/) and creator of Flyte, the popular open source AI orchestrator. He'll share his unique insights on the critical architectural decisions, operational best practices, and tooling required to take AI projects from experiment to production.

Before and after the discussion, connect with fellow platform engineers, MLOps practitioners, and technical leaders who are solving these exact problems. This is your chance to meet peers who are navigating the same technical and strategic hurdles of putting AI in production as you.

You’ll leave with actionable insights and a stronger network of AI builders. Topics will include:

* Bringing AI projects from experiment to production
* The MLOps blueprint for CI/CD in machine learning
* How AI Agents are changing the game for infrastructure and workflow management
* Building reliable, scalable, and cost-effective AI infrastructure
* Best practices for model training, versioning, and deployment workflows

Don't just listen. Learn and connect.

You must register on Partiful to attend: https://partiful.com/e/mvz6EiP3j9pdojKtDr6Y

We look forward to seeing you there! This event is a part of #SFTechWeek—a week of events hosted by VCs and startups to bring together the tech ecosystem. Learn more at [www.tech-week.com](http://www.tech-week.com/).

Building AI Together - San Francisco

Sage Elliott

AI & ML Engineer Hub: Happy Hour + Panel on Building Production AI - #SFTechWeek

San Francisco, CA 94101

**Octobers book is "LLMOps"!**

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/](https://slack.flyte.org/)

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-
About the book:
Title: LLMOps
Authors: Abi Aryan
Published: July 2025

https://learning.oreilly.com/library/view/llmops/9781098154196/

Chapters:
1\. Introduction to Large Language Models
2\. Introduction to LLMOps
3\. LLM\-Based Applications
4\. Data Engineering for LLMs
5\. Model Domain Adaptation for LLM\-Based Applications
6\. API\-First LLM Deployment
7\. Evaluation for LLMs
8\. Governance: Monitoring\, Privacy\, and Security
9\. Scaling: Hardware\, Infrastructure\, and Resource Management
10\. The Future of LLMs and LLMOps

**Book Description**
Here's the thing about large language models: they don't play by the old rules. Traditional MLOps completely falls apart when you're dealing with GenAI. The model hallucinates, security assumptions crumble, monitoring breaks, and agents can't operate. Suddenly you're in uncharted territory. That's exactly why LLMOps has emerged as its own discipline.
*LLMOps: Managing Large Language Models in Production* is your guide to actually running these systems when real users and real money are on the line. This book isn't about building cool demos. It's about keeping LLM systems running smoothly in the real world.

* Navigate the new roles and processes that LLM operations require
* Monitor LLM performance when traditional metrics don't tell the whole story
* Set up evaluations, governance, and security audits that actually matter for GenAI
* Wrangle the operational mess of agents, RAG systems, and evolving prompts
* Scale infrastructure without burning through your compute budget

https://learning.oreilly.com/library/view/llmops/9781098154196/

pandy yiankulidis

AI Book Club: LLMOps

Online event

**November'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/](https://slack.flyte.org/)

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-
About the book:
Title: Deep Learning for Biology
Authors: Charles Ravarani, Natasha Latysheva
Published: July 2025

https://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 Drug–Drug 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 Biology

**Book 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 you’re 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/

Building AI Together - Seattle

AI Book Club: Deep Learning for Biology

Union

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