AI Builders and Learners - Seattle

• A free Union.ai account (https://www.union.ai/)
• A GitHub account
• A Google account for Colab

More Session Details:
Part 1: MLOps/LLMOps & Effecent fine-tuning overview
Get introduced to the concepts around reproducible workflows, best practices for implementing efficient AI pipelines, and why parameter-efficient fine-tuning techniques such as Low-Rank Adaptation (LoRA) and QLoRA (Quantized Low-Rank Adaptation) are widespread.

Part 2: Build a scalable workflow and implement parameter-efficient fine-tuning [hands-on]
In this hands-on section, we'll walk through and run all the code to create our parameter-efficient fine-tuning workflow. We'll implement the following tasks:

• Download Dataset
• Download Model
• Visualize Dataset
• Fine-tune Model (LoRA & QLoRA)
• Evaluate Model Performance
• Perform Batch Inference

Part 3: Serve your fine-tuned LLM for real-time inference with a Streamlit UI
We'll pass our fine-tuned model artifact into an application using Streamlit to create a user interface for interaction.
After this section, you'll have the skills to build an end-to-end production-grade AI pipeline for fine-tuning and serving large language models (LLMs)

About the Speaker:
Sage Elliott is an AI Engineer with a background in computer vision, LLM evaluation, MLOps, IoT, and Robotics. He's taught thousands of people at live workshops. You can usually find him in Seattle biking around to parks or reading in cafes, catching up on the latest read for AI Book Club.
Connect with Sage: https://www.linkedin.com/in/sageelliott/

About Union.ai
Our AI workflow and inference platform unifies data, models and compute with the workflows of execution on a single pane of glass.

We also maintain Flyte, an open-source orchestrator that facilitates building production-grade data and ML pipelines.

💬 Join our AI and MLOps Slack Community: https://slack.flyte.org/
⭐ Check out Flyte on GitHub: https://github.com/flyteorg/flyte
🤝 Learn about everything else we’re doing at https://union.ai/

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 Science

Chapters:

• 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/

Want to discuss the contents during the reading month? Join the Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/

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About the book:
Title: The Hundred-Page Language Models Book: hands-on with PyTorch
Authors: Andriy Burkov
Published: January 15, 2025

https://thelmbook.com/

Chapters:

• Chapter 1. Machine Learning Basics
• Chapter 2. Language Modeling Basics
• Chapter 3. Recurrent Neural Network
• Chapter 4. Transformer
• Chapter 5. Large Language Model
• Chapter 6. Further Reading

Book Description
Large language models (LLMs) have fundamentally transformed how machines process and generate information. They are reshaping white-collar jobs at a pace comparable only to the revolutionary impact of personal computers. Understanding the mathematical foundations and inner workings of language models has become crucial for maintaining relevance and competitiveness in an increasingly automated workforce.
This book guides you through the evolution of language models, starting from machine learning fundamentals. Rather presenting transformers right away, which can feel overwhelming, we build understanding of language models step by step—from simple count-based methods through recurrent neural networks to modern architectures. Each concept is grounded in clear mathematical foundations and illustrated with working Python code.

In the largest chapter on large language models, you'll learn both effective prompt engineering techniques and how to finetune these models to follow arbitrary instructions. Through hands-on experience, you'll master proven strategies for getting consistent outputs and adapting models to your needs.

Learn more about the book here:
https://thelmbook.com/

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: Reinforcement Learning for Finance
Authors: Yves Hilpisch
Published: October 2024
https://learning.oreilly.com/library/view/reinforcement-learning-for/9781098169169/

Chapters:

• 1. Learning Through Interaction
• 2. Deep Q-Learning
• 3. Financial Q-Learning
• II. Data Augmentation
• 4. Simulated Data
• 5. Generated Data
• III. Financial Applications
• 6. Algorithmic Trading
• 7. Dynamic Hedging
• 8. Dynamic Asset Allocation
• 9. Optimal Execution
• 10. Concluding Remarks

Book Description
Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research.
This book is among the first to explore the use of reinforcement learning methods in finance.
Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems.
This book covers:

• Reinforcement learning
• Deep Q-learning
• Python implementations of these algorithms
• How to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocation

This book is the ideal reference on this topic. You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance.
Dr. Yves Hilpisch is founder and CEO of The Python Quants, a group that focuses on the use of open source technologies for financial data science, AI, asset management, algorithmic trading, and computational finance.

Learn more about the book here:
https://learning.oreilly.com/library/view/reinforcement-learning-for/9781098169169/