Principles and Practices of the Generative AI Life Cycle By YouAccel Training
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Principles and Practices of the Generative AI Life Cycle · Program
About this program
Principles and Practices of the Generative AI Life Cycle
Online Course
Learning format
Generative AI
Subcategory
English
Course language
$34.99
Price
Price may change · updated within 1–2 weeks
Official program page
What you'll learn in Principles and Practices of the Generative AI Life Cycle
- Key Phases of the GenAI Life Cycle: Understand the core stages of the generative AI life cycle and their significance in successful AI deployment.
- The Role of Governance in AI Projects: Learn about governance frameworks to ensure ethical and regulatory alignment throughout the AI life cycle.
- Problem Identification and Requirement Gathering: Explore strategies for defining problems and aligning GenAI solutions with business goals.
- Data Types and Acquisition Strategies: Gain insights into selecting and acquiring the right data for GenAI model development.
- Ensuring Data Quality and Ethics: Understand the importance of data accuracy, quality, and ethical considerations during the collection process.
- GenAI Model Design and Selection: Learn to select the most suitable generative AI models for different tasks and design custom models.
- Optimizing Model Performance: Discover techniques for tuning and optimizing models to achieve peak performance.
- Training Data Preparation and Monitoring: Explore how to prepare and select training data and monitor the training process to avoid common pitfalls.
- Deploying and Integrating GenAI Models: Learn best practices for integrating generative AI into existing systems and managing change effectively.
- Continuous Monitoring and Model Maintenance: Understand the tools and metrics needed to monitor performance and handle model drift over time.
- Data Privacy and Cybersecurity Measures: Gain insights into safeguarding models and data from cyber threats and ensuring compliance with privacy regulations.
- Auditing and Reporting AI Models: Learn to conduct performance audits, maintain transparency, and document AI life cycles for compliance.
- Managing AI Model Updates and Versions: Explore strategies for managing versions and implementing feedback loops for continuous improvement.
- Decommissioning AI Models: Understand when and how to retire models ethically while ensuring proper data and model archival strategies.
- User Feedback and Iterative Development: Learn to incorporate user feedback and manage iterative development cycles for ongoing improvements.
- Future Trends in GenAI Life Cycle Management: Gain insights into emerging technologies, AI governance trends, and innovations shaping the future of GenAI.
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