What we’re about
Welcome to the Building AI Together meetup!
💬 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 ⭐: 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 (4)
See all- Build Object Detection Pipelines & Computer Vision Applications - WorkshopLink visible for attendees
This workshop will equip you with the skills to effectively build your computer vision and ML pipelines using Python, PyTorch and Flyte/Union.
We'll cover data annotation for object detection, model fine-tuning, versioning, building an efficient AI pipeline, and deploy the trained computer vision model.--------------------------
REGISTER ON EVENTBRITE FOR THE LIVE LINK: https://www.eventbrite.com/e/build-object-detection-pipelines-computer-vision-applications-workshop-tickets-1417893069339
--------------------------Afterwards you'll be able to build your own computer vision object detection models, custom datasets, adn efficient AI pipelines! What you'll learn can also be transferred to different types of AI and ML pipelines.
The workflow we'll build hands-on together:
- Download Dataset
- Download pre-trained model weights
- Initialize object detection model
- Pre-process Data & Visualize Datasets
- Fine-tune model with PyTorch
- Evaluating Object Detection Models
- Save model to Hugging Face Hub
- Deploy model to the cloud (We'll also talk about edge deployment)
This workshop will cover:
- Data Annotation for Object Detection
- Computer Vision Models & Use cases
- Reliable & Reproducible ML pipelines
- Hands-on Building an ML pipeline
What you'll need to follow along:
- A free Union.ai account(https://www.union.ai/)
- A GitHub account for authentication
- A Google account to run code in Colab
Note: For ease of setup we'll be using Union's serverless AI orchestration platform for the workshop, but you can also implement computer vision pipelines in Flyte, the open-source platform on your own compute clusters. https://github.com/flyteorg/flyte
Who should attend:
Anyone interested in building better computer vision pipelines, AI applications and MLOps best practices should attend. This workshop is designed to be approachable for most skill levels. Familiarity with machine learning and Python is strongly encouraged but not required.
By the end of this workshop, you'll be able to build a reliable computer vision pipeline with Python, PyTorch, and Union. What you'll learn can be transferred to more complex AI pipelines and libraries.About the Speaker:
Sage an AI engineer and developer advocate who loves educating people and making ML applications more reliable. He's taught thousands of people during live workshops how to get started with Python, machine learning, computer vision, and AI observability.
Connect with Sage: https://www.linkedin.com/in/sageelliott/About Union.ai
Union is an AI platform powered by Flyte that simplifies ML infrastructure so you can develop, deploy, and innovate faster.
Write your code in Python, collaborate across departments, and enjoy full reproducibility and auditability. Union lets you focus on what matters.
💬 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/ - AI Book Club: Reinforcement Learning for FinanceLink visible for attendees
July's book is "Reinforcement Learning for Finance"!
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: 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/ - Fine-tune & Serve LLMs with LoRA & QLoRA for Production - LLMOps WorkshopLink visible for attendees
Build Scalable Workflows for Large Language Models (LLMs) and serve it in a hosted application!
REGISTER HERE TO GET THE LIVE LINK & RECORDING:
https://www.eventbrite.com/e/fine-tune-serve-llms-with-lora-qlora-for-production-llmops-workshop-tickets-1419481159359Training complex AI models at scale requires orchestrating multiple steps into a reproducible workflow and understanding how to optimize resource utilization for efficient fine-tuning. Modern MLOps and LLMOps tools help streamline these processes, improving the efficiency and reliability of your AI pipelines. This workshop will introduce you to the basics of MLOps and best practices for building efficient AI pipelines for large language models (LLMs).
By completing this workshop, you'll gain hands-on experience structuring scalable and reproducible AI workflows for fine-tuning LLMs using best practices such as caching, versioning, containerized resource utilization, parameter-efficient fine-tuning (PEFT), and more. We'll use Hugging Face for transformers and datasets, PEFT for implementing LoRA and QLoRA, bitandbytes for quantization, and union.ai for scalable workflows, GPUs, and serving our fine-tuned model.
This workshop will cover:
- MLOps / LLMOps pipeline basics
- Fine-tune a Hugging Face LLM model with LoRA & QLoRa
- Build a scalable and reproducible production grade workflow
- Deploy (Serve) your fine-tuned LLM in a real-time streamlit app
- Concepts covered can transfer to more complex pipelines and models
What you'll need to follow along:
- A free Union.ai account (https://www.union.ai/)
- A GitHub account
- A Google account for Colab
More Session Details:
Part 1: MLOps/LLMOps & Efficient 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/ - AI Book Club: Building Agentic AI SystemsLink visible for attendees
August's book is "Building Agentic AI Systems"!
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/
-------------------------------------------------
About the book:
Title: Building Agentic AI Systems
Authors: Anjanava Biswas, Wrick Talukdar
Published: April 2025https://learning.oreilly.com/library/view/building-agentic-ai/9781803238753/
or on packt: https://www.packtpub.com/en-us/product/building-agentic-ai-systems-9781801079273
Part 1: Foundations of Generative AI and Agentic Systems
Chapter 1: Fundamentals of Generative AI
Chapter 2: Principles of Agentic Systems
Chapter 3: Essential Components of Intelligent Agents
Part 2: Designing and Implementing Generative AI-Based Agents
Chapter 4: Reflection and Introspection in Agents
Chapter 5: Enabling Tool Use and Planning in Agents
Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach
Chapter 7: Effective Agentic System Design Techniques
Part 3: Trust, Safety, Ethics, and Applications
Chapter 8: Building Trust in Generative AI Systems
Chapter 9: Managing Safety and Ethical Considerations
Chapter 10: Common Use Cases and Applications
Chapter 11: Conclusion and Future OutlookBook Description
Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systems#### Key Features
- Understand the foundations and advanced techniques of building intelligent, autonomous AI agents
- Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems
- Explore crucial aspects of trust, safety, and ethics in AI agent development and applications
- Purchase of the print or Kindle book includes a free PDF eBook
#### Book Description
Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks.
Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents.
Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.#### What you will learn
- Master the core principles of GenAI and agentic systems
- Understand how AI agents operate, reason, and adapt in dynamic environments
- Enable AI agents to analyze their own actions and improvise
- Implement systems where AI agents can leverage external tools and plan complex tasks
- Apply methods to enhance transparency, accountability, and reliability in AI
- Explore real-world implementations of AI agents across industries
#### Who this book is for
This book is ideal for AI developers, machine learning engineers, and software architects who want to advance their skills in building intelligent, autonomous agents. It's perfect for professionals with a strong foundation in machine learning and programming, particularly those familiar with Python and large language models. While prior experience with generative AI is beneficial, the book covers foundational concepts for those new to agentic systems.
Learn more about the book here:
https://learning.oreilly.com/library/view/building-agentic-ai/9781803238753/or on packt: https://www.packtpub.com/en-us/product/building-agentic-ai-systems-9781801079273
