This unit focuses on taking trained models and AI services into production and operationalizing them at enterprise scale. Students learn deployment patterns, MLOps practices (monitoring, metrics, and automation), AI maturity and change management, and how to evaluate and implement generative and specialized AI services for business use cases.
Learning Objectives
- Analyze organizational AI maturity and readiness by completing an AI maturity assessment and identifying critical enablers and gaps for enterprise adoption
- Apply deployment and MLOps strategies to design a reproducible model deployment plan that synchronizes architecture and configuration across environments and specifies components of an ML/DL platform
- Demonstrate implementation choices for generative AI and specialized AI services by mapping use cases, completing a use-case identification sheet, and producing an organizational change management plan
- Evaluate model health by selecting and tracking key performance metrics, outlining monitoring strategies, and creating an ML operations automation guide for ongoing model maintenance and data preprocessing workflows
Curriculum
- 1 Section
- 24 Lessons
- 30 hours
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- Unit 2 — Deploying and Operationalizing AI24
- 1.1DEPLOYING YOUR MODELS20 mins
- 1.2KEY STRATEGIES FOR MONITORING ML MODELS20 mins
- 1.3THE AI MATURITY FRAMEWORK20 mins
- 1.4THE POWER OF GENERATIVE AI/ChatGPT FOR BUSINESS TRANSFORMATION AND INNOVATION20 mins
- 1.5THE PRODUCTIVITY REVOLUTION20 mins
- 1.6GET READY: AI TRANSFORMATION IS MORE CHALLENGING THAN DIGITAL TRANSFORMATION20 mins
- 1.7USE CASES TO CHOOSE FROM20 mins
- 1.8CORE AI SERVICES20 mins
- 1.9WORKBOOK TEMPLATE: ORG CHANGE MANAGEMENT PLAN20 mins
- 1.10KEY COMPONENTS OF AN ML AND DL PLATFORM20 mins
- 1.11WORKBOOK TEMPLATE: ML OPERATIONS AUTOMATION GUIDE20 mins
- 1.12THE DATA PROCESSING PHASES OF THE ML LIFECYCLE20 mins
- 1.13DATA-DRIVEN PREDICTIONS USING MACHINE LEARNING20 mins
- 1.14SYNCHRONIZING ARCHITECTURE AND CONFIGURATION ACROSS ENVIRONMENTS20 mins
- 1.15TRACKING KEY MODEL PERFORMANCE METRICS20 mins
- 1.16WORKBOOK TEMPLATE: AI MATURITY ASSESSMENT TOOL20 mins
- 1.17IMPLEMENTING GENERATIVE AI AND ChatGPT20 mins
- 1.18CRITICAL ENABLERS20 mins
- 1.19CHOOSING BETWEEN SMALLER PoC POINT SOLUTIONS AND LARGE-SCALE AI INITIATIVES20 mins
- 1.20WORKBOOK TEMPLATE: USE CASE IDENTIFICATION SHEET20 mins
- 1.21SPECIALIZED AI SERVICES20 mins
- 1.22SUMMARY20 mins
- 1.23KEY COMPONENTS OF AN ENTERPRISE AI/ML HEALTHCARE PLATFORM20 mins
- 1.24UNDERSTANDING THE DATA EXPLORATION AND PREPROCESSING STAGE20 mins
Instructor
Free
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