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AI Project Management Course

Original price was: INR ₹4,998.00.Current price is: INR ₹2,499.00.

The AI Project Management course is a 3-week program designed to help professionals manage AI-driven projects effectively. Learn to oversee AI development, ensure timely delivery, and manage multidisciplinary teams while addressing the unique challenges of AI project execution.

Aim

This program provides a comprehensive understanding of managing AI-based projects, focusing on the unique lifecycle, challenges, and methodologies specific to AI implementations. It equips participants with the skills to oversee AI projects from concept through deployment and beyond.

Program Objectives

  • Master AI Project Management: Learn to effectively manage AI projects across all phases.
  • Understand AI Challenges: Gain insights into the unique challenges of AI implementation.
  • Develop Risk Management Strategies: Address ethical, regulatory, and data quality risks in AI projects.
  • Lead AI Teams: Acquire skills to guide cross-functional teams of data scientists, engineers, and domain experts.
  • Align AI with Business Goals: Learn how to ensure AI projects are aligned with long-term business success.

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.

Participant Eligibility

  • Project Managers: Professionals responsible for overseeing AI projects.
  • AI Engineers: Technologists working on building and deploying AI models.
  • Product Managers: Those involved in developing AI-driven products.
  • IT Professionals: Individuals leading digital transformation efforts incorporating AI.

Program Outcomes

  • Expertise in AI Project Management: Gain the ability to manage AI projects from concept to deployment.
  • Risk Management Skills: Learn to handle AI project risks, including compliance and ethical issues.
  • Leadership in AI Teams: Acquire skills to guide AI development teams and integrate AI solutions into business strategies.
  • Lifecycle Management: Master the complete AI project lifecycle, ensuring successful implementation and continuous monitoring.

Program Deliverables

  • e-LMS Access: Full access to all course materials and resources online.
  • Real-Time Projects: Hands-on experience in managing AI projects.
  • Project Guidance: Expert support for your AI project plan and execution.
  • Research Publication: Opportunities for publishing research on AI project management.
  • Final Examination: Certification awarded based on mid-term assignments and final project submissions.
  • e-Certification: Digital certification provided upon successful completion of the course.

Future Career Prospects

  • AI Project Manager: Manage AI initiatives in tech companies or enterprises.
  • AI Product Manager: Oversee the development of AI-driven products and solutions.
  • Technical Program Manager for AI: Coordinate the technical execution of AI projects.
  • AI Implementation Consultant: Help companies deploy and manage AI solutions.
  • AI Strategy and Risk Manager: Develop strategies for AI adoption while managing risks and ensuring compliance.
  • Innovation Manager for AI Systems: Lead AI-driven innovation within organizations.

Job Opportunities

  • Companies adopting AI technologies across industries like finance, healthcare, and manufacturing.
  • AI Startups and consulting firms delivering AI solutions.
  • Enterprises incorporating AI into their digital transformation strategies.
MODE

Online/ e-LMS

TYPE

Self Paced

LEVEL

Moderate

DURATION

3 Weeks

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Certification

  • Upon successful completion of the workshop, participants will be awarded a Certificate of Completion, validating their skills and knowledge in advanced AI ethics and regulatory frameworks. This certification can be added to your LinkedIn profile or shared with employers to demonstrate your commitment to ethical AI practices.

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