LLM lifecycle mastery.

Format:

Video

Contributor:

Labonne, Maxime, instructor.

Yeh, Tom, instructor.

Griciūnas, Aurimas, instructor.

Mona, Mona, instructor.

Abdelreheem, Eslam Kamal, instructor.

Packt Publishing, publisher.

Language:

English

Subjects (All):

Natural language processing (Computer science).

Artificial intelligence.

Physical Description:

1 online resource (1 video file (3 hr., 26 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Birmingham, United Kingdom] : Packt Publishing, 2025.

Summary:

In this 3-hour course, you'll master the full lifecycle of large language models--from scaling and fine-tuning to deployment and beyond. Guided by leading AI experts, you'll explore LLMOps best practices, synthetic data strategies, and advanced techniques to make your AI systems more robust, efficient, and production-ready. What I will be able to do after this course Build monitoring and observability into LLM-based applications Apply LLMOps best practices for scalable and reliable deployments Set up reproducible pipelines and CI/CD for model updates Implement guardrails and human-in-the-loop strategies for safer AI Manage evaluation, drift detection, and prompt versioning effectively Course Instructor(s) This course is led by top AI experts from Meta, Google, Liquid AI, and academia. Collectively, they bring decades of experience in LLMs, MLOps, and generative AI, contributing to products like GitHub Copilot, Ray-Ban Meta smart glasses, and open-source frameworks. Their combined insights bridge cutting-edge research with practical implementation. Who is it for? This course is ideal for AI engineers, data scientists, and MLOps professionals seeking to master LLM development and operations. It's also valuable for researchers and builders aiming to connect theory with real-world deployment and optimization.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

1-80669-691-6

OCLC:

1550466968