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.
Acquérir des compétences de haut niveau avec Coursera Plus pour 199 $ (régulièrement 399 $). Économisez maintenant.
Expérience recommandée
Compétences que vous acquerrez
- Catégorie : Estimation
- Catégorie : Decision Making
- Catégorie : Project Schedules
- Catégorie : Dependency Analysis
- Catégorie : Milestones (Project Management)
- Catégorie : Smart Goals
- Catégorie : Risk Management
- Catégorie : Project Controls
- Catégorie : Project Performance
- Catégorie : Project Scoping
- Catégorie : Verification And Validation
- Catégorie : Project Planning
- Catégorie : Project Management
Détails à connaître
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décembre 2025
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Il y a un module dans ce cours
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.
Inclus
5 vidéos3 lectures4 devoirs
Instructeur
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Statut : Prévisualisation
Statut : PrévisualisationJohns Hopkins University
Statut : Prévisualisation
Statut : Prévisualisation
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