Data Science Dojo - DC

As generative AI systems continue to advance, traditional diffusion models often introduce complexity through iterative denoising and carefully tuned noise schedules. Flow Matching offers a more intuitive alternative by learning continuous transformations that move noise directly toward data, enabling faster sampling and simpler training.
In this session, we’ll introduce the fundamentals of Flow Matching and explain how it differs from diffusion-based approaches in both theory and practice. You’ll gain a clear understanding of why this method is gaining traction in state-of-the-art systems such as Stable Diffusion 3 and Meta’s Movie Gen, and where it fits in the broader generative AI landscape.
Through visual explanations and a hands-on code walkthrough, we’ll demonstrate how a model can be trained from scratch to transport noise into structured data. By the end of the session, you’ll have a solid conceptual foundation and practical starting points for exploring this approach in your own generative AI projects.

## 🛠️ What We’ll Cover:

• Core Concepts – An intuitive explanation of Flow Matching and how it compares to diffusion models
• Modeling Approaches Compared – Strengths, trade-offs, and why straight transport paths improve efficiency
• Real-World Use Cases – Applications in image generation, video synthesis, audio modeling, and molecular design
• Key Mechanics – Velocity fields, continuous flows, and simplified training objectives
• Hands-On Demo – A step-by-step notebook walkthrough using a toy dataset with visualized particle movement
• Efficiency Benefits – Reduced sampling steps, simpler objectives, and maintained output quality
• Getting Started – Recommended papers, tools, and libraries to begin experimenting

## 🔍 Why Attend?

• Understand modern generative modeling beyond traditional diffusion methods
• Learn how newer approaches improve efficiency without added complexity
• See practical, code-driven explanations instead of purely theoretical slides
• Leave with clear guidance and resources you can apply immediately

AI coding tools like Claude Code and Cursor are rapidly evolving from simple autocomplete assistants into autonomous, multi-step problem solvers. While adoption is accelerating across millions of developers, most custom agent projects still struggle to reach production. What separates experimental builds from reliable systems?
This session, presented by SambaNova, dives into the foundations of Deep Agent Architecture and explains what makes a “deep agent” fundamentally different from a traditional chatbot or basic LLM chain. You’ll gain a clear understanding of the structural patterns that power modern coding agents – and why orchestration, memory, tools, evaluation, and agent skills form the backbone of production-ready AI agents.
Through practical examples and a live demo, you’ll see how advanced agents decide what to do next, maintain long-term context, and coordinate multiple components without losing track. You’ll also learn a structured 6-step development process designed to help teams design, evaluate, and deploy agents with confidence.
By the end of the session, you’ll have both a strong conceptual framework and actionable guidance for implementing Deep Agent Architecture in real-world AI systems.

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### 🛠️ What We’ll Cover:

• Core Concepts – What defines a deep agent and how it differs from simple chatbots
• Architectural Foundations – The 5 Pillars behind Deep Agent Architecture
• Decision-Making Patterns – How agents plan, reason, and select next actions
• Multi-Agent Coordination – When distributed agent systems improve reliability
• Evaluation Strategies – The Smart Intern Test and structured validation methods
• Live Demo – Comparing a basic chain versus a full agent on the same task, featuring SambaNova technology
• Production Roadmap – A 6-step process for building reliable AI agents

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### 🔍 Why Attend?

• Understand why many agent systems fail before production
• Learn practical Deep Agent Architecture patterns used in real systems
• See a hands-on comparison between simple and advanced agent workflows, powered by SambaNova
• Leave with a clear roadmap for building production-ready AI agents