This unit introduces the foundational concepts, organizational considerations, and technical patterns required to adopt AI at enterprise scale. Students will learn to identify and prioritize use cases, apply responsible AI principles, and design people-centered MLOps and cloud-based reference architectures that prepare them for hands-on implementation in subsequent units.
Learning Objectives
- Analyze common organizational, technical, and ethical challenges companies face when implementing enterprise-wide AI and summarize mitigation strategies aligned to the pillars of responsible AI.
- Evaluate potential AI/ML use cases using a structured use-case identification and prioritization process and complete a prioritization worksheet to recommend the top initiatives for business impact.
- Apply roles-and-responsibilities frameworks, a team-building matrix, and governance templates to develop a people-centric implementation plan that aligns stakeholders and operational processes for an AI project.
- Demonstrate understanding of cloud-based AI reference architectures, MLOps practices, model operationalization, monitoring approaches, and selection criteria for AI/ML services and algorithms by producing a reference architecture and monitoring plan for a given use case.
Curriculum
- 1 Section
- 25 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 1 — Foundations of Enterprise AI Adoption25
- 1.1CHALLENGES FACED BY COMPANIES IMPLEMENTING ENTERPRISE-WIDE AI20 mins
- 1.2THE PILLARS OF RESPONSIBLE AI20 mins
- 1.3THE USE CASE IDENTIFICATION PROCESS FLOW20 mins
- 1.4BENEFITS AND FACTORS TO CONSIDER WHEN CHOOSING AN AI/ML SERVICE20 mins
- 1.5SUCCEEDING THROUGH A PEOPLE-CENTRIC APPROACH20 mins
- 1.6UNDERSTANDING THE ROLES AND RESPONSIBILITIES IN AN ML PROJECT20 mins
- 1.7REFERENCE ARCHITECTURE PATTERNS FOR TYPICAL USE CASES20 mins
- 1.8CENTRAL ROLE OF MLOps IN BRIDGING INFRASTRUCTURE, DATA, AND MODELS20 mins
- 1.9UNDERSTANDING YOUR DATA NEEDS20 mins
- 1.10BACK TO THE BASICS: WHAT IS ARTIFICIAL INTELLIGENCE?20 mins
- 1.11STANDARDIZING MODEL DEPLOYMENT, MONITORING, AND GOVERNANCE20 mins
- 1.12MONITORING MODELS20 mins
- 1.13SCALING AI TO BECOME AN AI-FIRST COMPANY20 mins
- 1.14THE RISE AND REACH OF GENERATIVE AI20 mins
- 1.15EMERGING AI TRENDS20 mins
- 1.16HOW DIGITAL NATIVES TACKLE AI ADOPTION20 mins
- 1.17WORKBOOK TEMPLATE: RESPONSIBLE AI DESIGN TEMPLATE20 mins
- 1.18PRIORITIZING YOUR USE CASES20 mins
- 1.19AWS AI AND ML SERVICES20 mins
- 1.20ALIGNING YOUR ORGANIZATION AROUND AI ADOPTION TO ACHIEVE BUSINESS OUTCOMES20 mins
- 1.21WORKBOOK TEMPLATE: TEAM BUILDING MATRIX20 mins
- 1.22FACTORS TO CONSIDER WHEN BUILDING AN ML PLATFORM20 mins
- 1.23MODEL OPERATIONALIZATION20 mins
- 1.24BENEFITS AND CHALLENGES OF CLOUD-BASED DATA PROCESSING20 mins
- 1.25FACTORS TO CONSIDER WHEN CHOOSING A MACHINE LEARNING ALGORITHM20 mins
Instructor
Free
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