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

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.

Installation guide

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