About the Course:

In this hands-on, live training, participants will explore the intersection of Machine Learning Operations (MLOps) and Generative AI. The session will cover the entire lifecycle of AI models, from data collection and model training to deployment, monitoring, and scaling. We’ll dive into the unique challenges posed by generative models, like GPT and DALL·E, and explore the tools, practices, and frameworks needed to manage these models at scale. Participants will leave with practical knowledge of MLOps techniques and strategies that can be immediately applied to generative AI projects.

Course Objective:

• Understand the fundamental concepts and importance of MLOps in generative AI.
• Learn best practices for deploying, monitoring, and scaling generative AI models.
• Gain hands-on experience with MLOps tools for versioning, testing, and automating model pipelines.
• Explore the lifecycle of generative AI projects, including data handling, model training, and continuous delivery.
• Learn how to implement security, compliance, and ethical considerations in MLOps for generative AI.
• Develop strategies for managing model performance, drift, and monitoring in real-time production environments.

Who is the Target Audience?

• AI Engineers and Data Scientists
• MLOps Engineers
• Software Engineers working with AI systems
• Machine Learning/AI Researchers
• DevOps professionals interested in MLOps
• Product Managers and CTOs working with AI-powered products

Basic Knowledge:

• Basic understanding of machine learning and AI concepts
• Familiarity with Python and machine learning frameworks (TensorFlow, PyTorch, etc.)
• Basic knowledge of DevOps practices and cloud platforms (AWS, GCP, Azure)
• Understanding of software development lifecycle (SDLC) concepts