WA3384

Leading AI ML Integration and Deployment of Projects Training

This Artificial Intelligence (AI) and Machine Learning (ML) training course teaches attendees the practical skills they need to lead the integration and deployment of AI/ML models into their real-world projects successfully.

Course Details

Duration

1 day

Prerequisites

  • Basic understanding of machine learning and artificial intelligence concepts
  • Familiarity with project management principles and practices
  • Some knowledge of software development lifecycle
  • Basic programming skills (preferred but not mandatory)

Target Audience

  • Project Managers and Leaders
  • AI/ML Engineers and Data Scientists
  • IT Managers and Executives
  • Technical Leads and Architects
  • Anyone involved in AI/ML integration and deployment projects

Skills Gained

  • Understand the scope and importance of AI/ML integration and deployment projects
  • Develop skills in leading and managing AI/ML projects effectively
  • Gain insights into the ethical and legal considerations of AI/ML projects
  • Familiarize with the tools and technologies used in AI/ML integration and deployment
  • Learn from real-world case studies and best practices
Course Outline
  • Introduction
    • Course Overview
    • Importance of AI/ML Integration and Deployment Projects
  • Understanding AI/ML Integration and Deployment Projects
    • Definition and Scope
    • Key Concepts and Terminologies
    • Common Challenges and Best Practices
  • Leading AI/ML Integration and Deployment Projects
    • Role and Responsibilities of a Project Leade
    • Effective Team Management Strategies
    • Stakeholder Engagement and Communication Techniques D. Collaboration and Alignment with Different Teams
  • Project Lifecycle for AI/ML Integration and Deployment
    • Planning and Goal Setting
    • Data Gathering and Preprocessing
    • Model Development and Evaluation 
    • Integration and Deployment E. Post-Deployment Monitoring and Maintenance
  • Ethical and Legal Considerations
    • Bias and Fairness in AI/ML Projects
    • Privacy and Security Measures
    • Regulatory Compliance
  • Tools and Technologies for AI/ML Integration and Deployment
    •  Model Training Platforms
    • Deployment Frameworks and Platforms
    • Monitoring and Evaluation Tools
  • Case Studies and Real-World Examples
    • Successful AI/ML Integration and Deployment Projects
    • Lessons Learned and Best Practices
  • Course Wrap-up and Next Steps
    • Recap of Key Learning Points
    • Further Resources and Learning Opportunities
    • Q&A Session