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Avis du cours Principles and Practices of the Generative AI Life Cycle par YouAccel Training

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Explore key concepts, methodologies, and best practices for every stage of the GenAI life cycle.

Format d'apprentissage Online Course

Sous-catégorie Generative AI

Prix du cours $34.99

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Explore key concepts, methodologies, and best practices for every stage of the GenAI life cycle.

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67,725 students

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English

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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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