AI Project Milestones with Confidence

There is 1 module in this course

Managing AI projects requires more than ambition; it requires precision in planning and evaluation. In this course, Learners will learn how to define clear, measurable milestones with exit criteria, map dependencies to uncover critical path risks, and evaluate milestone completion reports against scope, quality, and readiness standards. Through videos, readings, and hands-on practice, they’ll gain confidence in turning vague project goals into structured milestones that drive accountability. Learners will practice using tools like PERT charts to identify blockers, analyze real-world milestone conflicts such as GPU procurement delays, and work through case studies where they must decide whether to approve or reject milestone closure. By the end, learners will be able to create milestone schedules, anticipate risks, and make evidence-based go/no-go decisions that ensure AI projects stay on track and deliver results with quality.

What's included

5 videos3 readings4 assignments

5 videosTotal 14 minutes

Introduction and Welcome2 minutes
Why Milestones Fail Without Clear Criteria?3 minutes
The Critical Path Explained in AI Projects2 minutes
How to Evaluate AI Milestones: Scope, Quality, and Readiness3 minutes
Congratulations and Continuous Learning Journey1 minute

3 readingsTotal 26 minutes

Writing Effective Milestones with Exit Conditions9 minutes
Common AI Milestone Conflicts9 minutes
Case Study: Rejecting a Beta Release Report8 minutes

4 assignmentsTotal 95 minutes

Graded Quiz: AI Milestone Readiness Assessment20 minutes
HOL: PERT Chart Dependency and Risk Mapping25 minutes
HOL: Project Case: Critical Path Blocker Analysis25 minutes
HOL: Milestone Evaluation and Scoring Practice25 minutes

Instructor

ansrsource instructors

Coursera

5 Courses161 learners

Offered by

Coursera

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