Program Structure

Module 1: Introduction to AI Project Management

• Overview of AI projects: Characteristics, opportunities, and challenges.
• Differences between AI projects and traditional IT projects.
• Key roles in AI project teams (Data Scientists, ML Engineers, Domain Experts).

Module 2: AI Project Lifecycle

• Phases of an AI project: Problem definition, data collection, model development, deployment, and monitoring.
• Agile and iterative development for AI projects.
• Managing uncertainty and experimentation in AI workflows.

Module 3: AI Project Scoping and Planning

• Defining project goals, key performance indicators (KPIs), and business impact.
• Building a comprehensive data strategy: Data availability, quality, and preprocessing.
• Market research and competitive analysis for AI initiatives.

Module 4: Data Management and Preparation

• Data collection, labeling, and cleaning techniques for AI projects.
• Managing scalable data pipelines and workflows.
• Tools and technologies for efficient data management in AI.

Module 5: Resource Allocation and Team Management

• Building an AI project team with the right skills and roles.
• Managing resources: Infrastructure, tools, and talent.
• Collaborative tools for AI teams: Git, JIRA, Trello, Slack.

Module 6: Model Development and Experimentation

• Managing machine learning development cycles.
• Experimentation platforms for AI (MLflow, Weights & Biases).
• Hyperparameter tuning, model iterations, and continuous improvement.

Module 7: Risk Management in AI Projects

• Identifying and mitigating risks related to data quality, model drift, and bias.
• Navigating regulatory and ethical challenges in AI development.
• Tools for monitoring AI systems and managing failures.

Module 8: AI Model Evaluation and Performance Metrics

• Defining AI model performance metrics (e.g., accuracy, precision, recall, F1 score).
• Managing validation techniques such as cross-validation and performance tracking.
• Strategies for retraining models to address model drift over time.

Module 9: AI Deployment and Integration

• Deploying AI models into production environments.
• Continuous integration/continuous deployment (CI/CD) for AI systems.
• Monitoring and managing AI model performance post-deployment.

Module 10: AI Ethics, Fairness, and Regulatory Compliance

• Addressing bias and fairness in AI models.
• Legal and regulatory considerations in AI project management.
• Ensuring explainability and transparency in AI systems.

Module 11: Cost Management and ROI Analysis for AI Projects

• Budgeting for AI projects: Infrastructure, tools, talent, and data acquisition.
• Measuring the return on investment (ROI) of AI initiatives.
• Financial and time planning for AI projects.

Final Project

• Develop a comprehensive AI project plan, covering data strategy, team structure, timeline, risk management, and deployment strategy.
• Example: Plan the deployment of an AI-based recommendation system or a customer service chatbot.