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watsonx.ai: Large Language Model Operations
Course Description
Overview
This hands-on course introduces learners to the principles and practices of Large Language Model Operations (LLMOps), focusing on the development, deployment, and governance of AI models using IBM's watsonx.ai platform. Participants will explore the IBM LLM workflow, utilize Prompt Lab for model prompting, and develop Retrieval-Augmented Generation (RAG) models with AutoAI. The course emphasizes practical experience through UI-based labs, enabling learners to build, deploy, and monitor AI models effectively.
Objectives
- Understand the fundamentals of Large Language Model Operations (LLMOps) and their role in AI model lifecycle management
- Navigate and apply the IBM LLM workflow within the watsonx.ai platform
- Develop RAG models using AutoAI, integrating in-memory or external vector databases
- Leverage Prompt Lab to create and refine prompts for foundational models, focusing on tasks like summarization
- Deploy AI models and implement governance strategies using IBM's AI Governance tools to ensure compliance and transparency
Audience
AI specialists, data scientists, developers, or anyone interested interested in learning LLMOps using watsonx.ai
Topics
- Introduction
- Module 1: Large Language Model Operations
- Module 2: LLMOps on watsonx
- Module 3: Prompting a foundational model with Prompt Lab
- Module 4: Automating Retrieval Augmented Generation
- Module 5: Deploying the generative AI assets
- Module 6: Governing the generative AI assets
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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