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Fundamentals of Operationalizing AI

94-879

Units: 6

Description

Embark on a transformative journey with our course, "Fundamentals of Operationalizing AI: Mastering AI Lifecycle from Theory to Practice". This comprehensive program is meticulously designed to provide an in-depth understanding of the entire AI lifecycle. It covers a broad spectrum of topics, from business scoping, data management and engineering, to model development, deployment, and stewardship. 

 

The course introduces the concept of Operationalizing AI (OAI), a critical aspect of AI implementation that is often overlooked. You will learn about its significance, the challenges it presents, its benefits, and the roles involved. We delve into related disciplines such as DevOps, DataOps, DevSecOps, MLOps, and AI Ops, focusing on the emerging best practices, roles, skills, capabilities, and governance across the AI lifecycle.

 

Upon completion, you will have a robust understanding of how to build and manage large-scale, production-quality AI systems that seamlessly integrate data, software, and models. Several guest lectures from industry practitioners and companies developing tools for operationalizing AI will give you a window into how organizations are operationalizing AI and creating the necessary tooling. This course is an invaluable resource for those aspiring to master the art and science of AI implementation, offering practical insights and knowledge that can be immediately applied in the real world. Join us and equip yourself with the skills to navigate the exciting world of AI

 

Learning Outcomes

  1. Clearly explain the key components of the AI lifecycle, its maturity levels, and how they apply to various industries and functional areas.
  2. Identify and describe the different stages of the AI lifecycle, including the specific artifacts, roles, skills, and capabilities required at each stage.
  3. Create an end-to-end, top-down governance framework that ensures the consistent, efficient, and effective operation of AI systems.
  4. Identify business problem spaces and match with appropriate technology solution spaces and apply techniques for validating models.
  5. Assess the existing talent and skills within the organization and develop a plan to fill any identified gaps.
  6. Develop a comprehensive strategy for operationalizing AI, including considerations for the development lifecycle, model deployment, monitoring, and change management.
  7. Define and measure Key Performance Indicators (KPIs) for AI projects, calculate Return on Investment (ROI), and propose strategies for scaling and maintaining AI solutions.
  8. Prepare and deliver a compelling presentation that communicates a comprehensive AI lifecycle strategy to an executive audience, highlighting the business value and potential impact of the proposed approach.

Prerequisites Description

Basic understanding of statistics and machine learning is useful, but not necessary. 

Syllabus