Udemy – Principles and Practices of the Generative AI Life Cycle 2024-9

Published on: 2024-11-19 18:02:05

Categories: 28

Description

Course Principles and Practices of the Generative AI Life Cycle. This course provides a comprehensive overview of the generative artificial intelligence (GenAI) lifecycle, giving students a comprehensive understanding of the key principles and processes involved in developing, deploying, and maintaining GenAI models. The course is designed to provide a theoretical foundation and emphasizes the strategic aspects of each stage of the GenAI lifecycle to ensure that participants gain a clear view of how generative AI evolves from concept to deployment and beyond.

What you will learn

Key Stages of the GenAI Lifecycle: Understanding the main stages of the generative artificial intelligence life cycle and their importance in a successful AI deployment.
The role of governance in AI projects: learning about governance frameworks to ensure ethical and regulatory alignment throughout the AI lifecycle.
Problem Identification and Requirements Gathering: Exploring strategies for defining problems and aligning GenAI solutions with business goals.
Data Types and Data Acquisition Strategies: Gain insights into selecting and acquiring appropriate data for GenAI model development.
Ensuring data quality and ethics: Understanding the importance of data accuracy, quality and ethical considerations during the collection process.
GenAI model design and selection: Learning to choose the most suitable generative artificial intelligence models for different tasks and design custom models.
Optimizing Model Performance: Discover techniques for tuning and optimizing models to achieve maximum performance.
Training Data Preparation and Monitoring: Explore how to prepare and select training data and monitor the training process to avoid common errors.
Deploying and Integrating GenAI Models: Learn best practices for integrating generative AI into existing systems and effectively managing change.
Continuous monitoring and model maintenance: Understanding the tools and metrics necessary to monitor performance and address model drift over time.
Data privacy practices and cybersecurity: gain insight into protecting models and data from cyber threats and ensuring compliance with privacy regulations.
Auditing and Reporting AI Models: Learn to perform performance audits, maintain transparency, and document AI lifecycles for compliance.
Managing AI Model Updates and Versions: Exploring Version Management Strategies and Implementing Feedback Loops for Continuous Improvement.
Retiring AI models: Understanding when and how to ethically retire models while ensuring appropriate archiving strategies for data and models.
User Feedback and Iterative Development: Learn to integrate user feedback and manage iterative development cycles for continuous improvement.
Future Trends in GenAI Lifecycle Management: Gain insights into emerging technologies, AI governance trends, and innovations shaping the future of GenAI.

This course is suitable for people who

AI Enthusiasts and Technologists: People interested in understanding the full lifecycle of generative AI models and their practical applications.
Business leaders and executives: Professionals seeking to align AI capabilities with business strategies for innovation and competitive advantage.
Data scientists and AI developers: Those looking to deepen their knowledge of model selection, optimization, and integration in real-world contexts.
Governance and Compliance Officers: Individuals responsible for implementing AI governance frameworks and ensuring ethical compliance in AI systems.
IT and Systems Managers: Professionals who manage the deployment, monitoring, and maintenance of AI solutions across an organization’s infrastructure.