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Lead Instructor(s)
Date(s)
Oct 21 - 25, 2024
Registration Deadline
Location
Live Virtual
Course Length
5 Days
Course Fee
$4,750
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Deepen your understanding of the end-to-end AI system architecture needed to design and deploy large language models (LLMs)—and implement an LLM application of your own during a hands-on group project.

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS PART OF THE PROFESSIONAL CERTIFICATE PROGRAM IN MACHINE LEARNING & ARTIFICIAL INTELLIGENCE.

Course Overview

Finance. Healthcare. Manufacturing. AI is revolutionizing industries around the world, helping enterprises streamline workflows, optimize operations, power new product development—and boost their bottom lines. But designing and implementing AI applications effectively—in a way that drives value for your organization—requires a nuanced understanding of an end-to-end AI system architecture.

In this dynamic five-day course, led by renowned AI expert David Martinez, you’ll explore every stage of the AI system architecture building process—from data input to insights. These stages, or “building blocks,” support the key processing pipeline in the design of large language models (LLMs). You’ll then put your knowledge into action by working with accomplished global peers to implement an LLM application.

 

Learning Outcomes
  • Deepen your understanding of the AI system architecture building blocks needed to design LLMs
  • Acquire the tools, skills, and strategies to successfully deploy LLMs
  • Learn systems engineering principles, including logistics, testing and evaluation, and maintainability
  • Explore responsible AI as a way to design, develop, and deploy AI in a legal and ethical manner
  • Participate in a hands-on project, where you’ll design and implement an LLM application and then present it to AI practitioners

Program Outline

Day 1

  • Course introduction, AI background and system architecture building blocks

  • Systems engineering principles

  • Data conditioning

  • Time to prepare proposals for large language model application

  • Team proposal presentations

Day 2

  • Machine learning fundamentals

  • Performance evaluation techniques

  • Multi-layer perceptron (MLP) machine learning model

  • Larger language models (LLMs) primer

  • Introduction to LLM design on a contemporary AI platform

Day 3

  • Modern computing as enabling technology

  • Human-machine teaming

  • Panel: AI leaders and practitioners are invited to discuss LLMs challenges and opportunities

  • Responsible AI

  • Teams work on the implementation of LLM application

Day 4

  • Guidelines on deploying LLMs

  • Teams work on the implementation of LLM application (cont.)

  • Teams work on the implementation of LLM application (cont.)

  • Teams work on the implementation of LLM application (cont.)

  • Teams finalize the implementation of LLM application and prepare presentation

Day 5

  • Summary of material covered in class

  • Team class project presentation

  • Course wrap-up

Who Should Attend

This course is designed for participants who have a bachelor's degree (at a minimum) and at least three years of professional experience. Professionals who will particularly benefit from the curriculum include:

  • Systems engineers who need practical frameworks for building AI pipelines and launching new projects
  • Executives looking to manage change and make smart investment decisions related to AI technology
  • Technical leaders who require specialized knowledge to head effective AI-powered teams
  • Product directors who want to develop cohesive plans for product development and deployment
  • Team leaders who guide high-technology groups in executing complex AI initiatives
  • Researchers who want to explore and advance AI applications across industries
  • Entrepreneurs who need actionable roadmaps for building and growing AI-powered businesses