Essential Guide to LLMOps
Implementing effective strategies for Large Language Models in deployment and continuous improvement
Created by Ryan Doan
Explore the practical side of managing large language models in production environments. Learn how to streamline workflows, improve deployment, and ensure ethical, scalable AI. Build confidence in handling every stage of LLM operations, from data preparation to continuous improvement.
Packt | Jul 2024 | 190 min
What You Will Learn
You will build your skills step by step by working through real-world scenarios and hands-on strategies. Each topic focuses on actionable techniques for data handling, model development, deployment, and ongoing optimization. By applying these methods, you will gain the confidence to manage LLMs effectively in production.
Key Features
- Develop practical skills for deploying and maintaining large language models
- Use LLMOps tools to manage data, fine-tune models, and optimize workflows
- Apply best practices for scalability, governance, and responsible AI deployment
Target Audience
Designed for machine learning professionals, data scientists, and ML engineers with a solid foundation in AI. If you are looking to advance your expertise in deploying, maintaining, and optimizing large language models, this course will help you stay ahead in the fast-paced world of AI operations.
