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.

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**REGISTER ON EVENTBRITE FOR THE LIVE LINK:** https://www.eventbrite.com/e/build-object-detection-pipelines-computer-vision-applications-workshop-tickets-1417893069339
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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/](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](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/](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/](https://slack.flyte.org/)
⭐ Check out Flyte on GitHub:[ https://github.com/flyteorg/flyte](https://github.com/flyteorg/flyte)
🤝 Learn about everything else we’re doing at[ https://union.ai/](https://union.ai/)

Building AI Together - Seattle

Sage Elliott

Build Object Detection Pipelines & Computer Vision Applications - Workshop

Online event

**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/](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/

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AI Book Club: Reinforcement Learning for Finance

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