AI Project Management
Lead the Future: Master the Art of Managing AI Projects from Start to Finish
Skills you will gain:
This program covers AI project management frameworks, integration of AI technologies, risk mitigation, ethical considerations, and collaboration with data scientists and AI engineers. It also includes practical aspects like setting realistic goals, monitoring project progress, and aligning AI projects with business objectives.
Aim: To provide a comprehensive understanding of managing AI-based projects, focusing on the lifecycle, challenges, and methodologies specific to AI projects. The program equips participants with skills to oversee AI implementations from conception to deployment.
Program Objectives:
- Learn to manage AI-based projects effectively across all phases.
- Understand the unique challenges in AI implementation.
- Develop risk management strategies for AI ethics and compliance.
- Gain skills to lead cross-functional AI project teams.
- Align AI projects with business goals for long-term success.
What you will learn?
Modules for AI Project Management:
- Introduction to AI Project Management
- Overview of AI Projects: Characteristics and Challenges
- Differences between AI Projects and Traditional IT Projects
- Key Roles in AI Project Management (Data Scientists, ML Engineers, Domain Experts)
- AI Project Lifecycle
- Phases of an AI Project: Problem Definition, Data Collection, Model Development, and Deployment
- Agile and Iterative Development for AI Projects
- Managing Uncertainty and Experimentation in AI
- AI Project Scoping and Planning
- Defining AI Project Goals and KPIs
- Understanding Business Problems and AI Solutions
- Building a Data Strategy: Data Availability, Quality, and Preprocessing
- Data Management and Preparation
- Data Collection, Labeling, and Cleaning for AI Projects
- Managing Data Pipelines and Workflows
- Tools and Techniques for Scalable Data Management
- Resource Allocation and Team Management
- Building AI Project Teams: Skills and Roles
- Managing Resources: Tools, Infrastructure, and Talent
- Collaborative Tools for AI Teams: Git, JIRA, Trello, Slack
- Model Development and Experimentation
- Managing Machine Learning and AI Development Cycles
- Experimentation Platforms for AI (e.g., MLflow, Weights & Biases)
- Managing Hyperparameter Tuning and Model Iterations
- Risk Management in AI Projects
- Identifying and Mitigating Risks in AI Projects (Data Quality, Model Drift, Bias)
- Regulatory and Ethical Challenges in AI
- Tools for Monitoring AI Systems and Managing Failures
- AI Model Evaluation and Performance Metrics
- Defining Metrics for AI Model Performance (Accuracy, Precision, Recall, F1 Score)
- Managing Model Validation and Cross-Validation
- Performance Tracking over Time: Model Drift, Retraining Strategies
- AI Deployment and Integration
- Deploying AI Models in Production Environments
- Continuous Integration and Deployment (CI/CD) for AI
- AI Model Monitoring and Performance Management
- 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 of AI Systems
- Cost Management and ROI Analysis for AI Projects
- Budgeting for AI Projects: Cost of Infrastructure, Tools, and Talent
- Measuring ROI of AI Projects: Balancing Innovation and Business Value
- Financial and Time Planning for AI Initiatives
Intended For :
Project managers, AI engineers, product managers, and IT professionals aiming to manage AI-driven initiatives.
Career Supporting Skills