Monday, June 17, 2024
ad Launches Free LLMOps Course: Master LLM Customization and Deployment Launches Free LLMOps Course: Master LLM Customization and Deployment

Explore the Art of Fine-Tuning Language Models with's Comprehensive LLMOps Course., a leading online education platform, has announced the release of a free course focused on Large Language Model Operations (LLMOps). This cutting-edge course, led by Erwin Huizenga, Machine Learning Technical Lead at Google, is designed to empower learners with the skills needed to fine-tune and deploy custom Large Language Models (LLMs) for specific applications.

The course delves deep into the LLMOps pipeline, guiding students through the essential processes of pre-processing training data for supervised instruction tuning. Participants will learn how to adapt an open-source supervised tuning pipeline to train and deploy a custom LLM, making the course invaluable for anyone looking to create a specialized LLM workflow. An intriguing hands-on project involves developing a question-answer chatbot tailored to Python coding queries, providing practical experience in LLM application.

Key components of the course include:

  1. Retrieving and Transforming Training Data: Students will learn how to extract and modify data for the supervised fine-tuning of LLMs.
  2. Versioning Data and Models: The course emphasizes the importance of versioning to track tuning experiments effectively.
  3. Configuring and Executing Tuning Pipelines: Learners will configure an open-source supervised tuning pipeline, execute it for training, and then deploy the tuned LLM.
  4. Monitoring and Safety: A crucial part of the course is understanding how to output and analyze safety scores to responsibly monitor and manage the behavior of LLM applications.

The course offers practical experience with tools like BigQuery data warehouse, Kubeflow Pipelines, and Google Cloud, ensuring that participants gain hands-on skills relevant to current industry standards.

Targeted at anyone interested in tuning LLMs and building LLMOps pipelines, the course is particularly beneficial for those aspiring to learn best practices in data and model versioning, preprocess large datasets within a data warehouse, and implement responsible AI practices by monitoring safety scores.

With its comprehensive curriculum and expert instruction, this course from represents a significant opportunity for learners worldwide to advance their skills in the rapidly evolving field of AI and language model operations.

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Analytics Drift
Analytics Drift
Editorial team of Analytics Drift


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