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DeepLearning.AI

Erwin Huizenga

In this course, you will learn to retrieve and transform training data for supervised fine-tuning of an LLM, version your data and tuned models to track your tuning experiments, configure an open-source supervised tuning pipeline, and execute that pipeline to train and deploy a tuned LLM. Additionally, you will learn to output and study safety scores to responsibly monitor and filter your LLM application’s behavior. Tools you’ll practice with include BigQuery data warehouse, the open-source Kubeflow Pipelines, and Google Cloud.

LLMOps

Overview of the LLMOps pipeline and its importance in AI applications.

Introduction to LLMOps

Learn to retrieve and transform training data for supervised fine-tuning of an LLM.

Pre-processing Training Data

Best practices for versioning your data and tuned models to track your tuning experiments.

Data and Model Versioning

How to configure an open-source supervised tuning pipeline.

Configuring the Tuning Pipeline

Steps to execute the pipeline to train and deploy a tuned LLM.

Executing the Tuning Pipeline

Output and study safety scores to responsibly monitor and filter your LLM application’s behavior.

Safety Score Monitoring

Try out the tuned and deployed LLM yourself in the classroom.

Hands-on Practice

Practice with tools like BigQuery data warehouse, Kubeflow Pipelines, and Google Cloud.

Tools and Technologies

This course guides you through the LLMOps pipeline, teaching you how to pre-process training data for supervised instruction tuning and adapt a supervised tuning pipeline to train and deploy a custom LLM. Learn best practices, including versioning your data and models, and pre-process large datasets inside a data warehouse.

Anyone who wants to learn to tune an LLM and build an LLMOps pipeline.

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LLMOps

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