AI Engineering Workshop
Live in Seattle
A weekend of immersive learning
May 10th 4:00 PM to May 12th 9:00 PM
Join us at
Archer Hotel in Redmond, WA
( Food and Drinks are included )
For Builders, by Builders
Through a series of hands-on projects and deep-dives, participants are immersed into real-world challenges and breakthroughs that define modern AI development. It’s an environment where the tools meet the boundless possibilities of AI, encouraging not just learning, but the actual doing.
Workshop Highlights
Step-by-step Recipes
Learn ready-to-use recipes to quickly identify use cases, build solutions, and deliver customer value through a rapid iterative approach.
Hands-On Projects
Engage in practical, real-world AI projects to apply what you’ve learned in a tangible way.
Community Connection
Network with a vibrant community of professionals, enthusiasts, and peers who share your passion for AI.
Rise above the noise
Our curriculum is designed to cut through the noise, focusing on actionable skills and real-world applications. We aim to empower you with the ability to sift through the evolving AI landscape, distinguishing fleeting trends from transformative innovations.
You’ll learn not just to navigate but to innovate, employing rapid iterations to swiftly bring ideas to life and deliver substantial value to customers.
"AI won’t replace Humans – but Humans with AI will replace Humans without AI ."
Karim R. Lakhani Professor, Harvard Business School
Join Us
Al’s ever-changing field levels the playing field for those driven by curiosity and innovation. Our workshop is open to all – from expert engineers to eager high schoolers. In Al’s fast-paced realm, fresh ideas matter as much as experience.
We aim to create a welcoming space for learning and innovation, regardless of background.
Recommended prerequisites: Some Python knowledge, familiarity with software development, and a basic understanding of linear algebra.
Curriculum
Begin your journey with an overview of how AI is revolutionizing application development, setting the stage for the transformative skills you will acquire.
Dive into the diverse technologies underpinning AI, from machine learning models to the latest in AI research, understanding their applications and potential.
Unpack the fundamentals of deep learning, gaining insights into how these powerful models learn from data to make predictions and drive decision-making.
Learn to categorize, prioritize and spot real-world problems that AI can solve today. We'll guide you through the process of identifying opportunities where AI can deliver impactful solutions rapidly.
Discover how to design AI systems that are not only powerful but also customer-obsessed.
Get hands-on experience with OpenAI's suite of APIs, learning how to integrate advanced AI capabilities into your projects with ease.
Explore the world of LangChain, a framework for building more complex and useful AI applications, and see how it can amplify your development process.
Delve into the development of RAG solutions, which combine the power of retrieval-based and generative AI to provide more nuanced and accurate outputs.
Learn how to build distributed reinforcement learning solutions using Ray and RLlib, enabling your RL applications to scale across multiple machines for unprecedented computational power.
Schedule
04:00 PM
Registration and Check-in
04:30 PM
Ice breaker event
05:00 PM
Keynote
05:30 PM
In-person sessions with lectures, follow-along coding and practical work.
08:30 PM
Dinner (Networking and Q/A)
08:00 AM
Breakfast
08:30 AM
In-person sessions with lectures, follow-along coding and practical work.
12:30 PM
Lunch
01:30 PM
In-person sessions with lectures, follow-along coding and practical work.
07:30 PM
Q/A with Panelists
08:30 PM
Dinner (Networking and Q/A)
09:00 PM
Fun Event
09:30 PM
Open Work Time
08:00 AM
Breakfast
08:30 AM
In-person sessions with lectures, follow-along coding and practical work.
12:30 PM
Lunch
01:30 PM
In-person sessions with lectures, follow-along coding and practical work.
05:30 PM
Participants Project demo and feedback
08:30 PM
Dinner (Networking and Q/A)
All participants will stay Friday and Saturday nights at the same hotel, enhancing the immersive workshop experience. (Note: Participants under 18 must be accompanied by a parent or guardian.)
Throughout the weekend, nutritious meals, snacks, and drinks will be provided. Simply arrive ready for an intense learning journey, equipped with an eagerness to learn. Leave the rest to us.
Presenters
Pavan Kanaparthy
Venue
FAQs
No. The weekend sessions are crafted with the intention of providing an immersive, interactive experience that thrives on direct, in-person engagement.
While prior experience in AI or machine learning is beneficial, it is not required. The workshop caters to participants with varying levels of expertise, from beginners to experienced practitioners.
Our mission is to deliver exceptional value and foster a vibrant, long-lasting community of AI enthusiasts and professionals. In line with this commitment, we offer a 100% refund policy for any reasonable cause.
Sign up for updates to stay informed
Upcoming Courses
MLOps: Model lifecycle management, (CI/CD) for AI.
Review and investigate sites like huggingface.co models/datasets
Engage with the AI community, present at and attend conferences
Innovating New AI Models and Applications: Design thinking for AI, experimental methodologies.
Computer Vision: Object detection, image segmentation, face recognition.
Robotics: Control theory, sensors and actuators, SLAM.
AI Ethics and Bias: Fairness, accountability, transparency in AI.
Neural Networks: Architecture, activation functions, forward and backward propagation.
Convolutional Neural Networks (CNNs): Image recognition, video analysis.
Recurrent Neural Networks (RNNs): Time series analysis, natural language processing.
Generative Adversarial Networks (GANs): Image generation, style transfer.
Transformer Models: Attention mechanisms, BERT, GPT.
Data Processing: Handling, cleaning, and preprocessing data.
Exploratory Data Analysis: Visualization, understanding data features.
Supervised Learning: Regression, classification algorithms.
Unsupervised Learning: Clustering, dimensionality reduction.
Reinforcement Learning: Q-learning, policy gradients.
Machine Learning Best Practices: Cross-validation, feature engineering, hyperparameter tuning
Programming Fundamentals: Python, R, or Julia for AI.
Data Structures and Algorithms: For efficient data manipulation and analysis.
Software Development Practices: Version control (Git), testing, debugging, and containerization (Docker).
Linear Algebra: Vectors, matrices, eigenvalues, and eigenvectors.
Calculus: Differential and integral calculus, partial derivatives.
Probability and Statistics: Probability theory, distributions, statistical testing, Bayesian thinking.
Discrete Mathematics: Logic, set theory, combinatorics.
Optimization: Gradient descent, convex optimization.
