Free for a limited time
- Adapt an open source pipeline that applies supervised fine-tuning on an LLM to better answer user questions.
- Learn best practices, including versioning your data and your models, and pre-process large datasets inside a data warehouse.
- Learn responsible AI by outputting safety scores on sub-categories of harmful content.
What you’ll learn in this course
In this course, you’ll go through the LLMOps pipeline of pre-processing training data for supervised instruction tuning, and adapt a supervised tuning pipeline to train and deploy a custom LLM. This is useful in creating an LLM workflow for your specific application. For example, creating a question-answer chatbot tailored to answer Python coding questions, which you’ll do in this course.
Through the course, you’ll go through key steps of creating the LLMOps pipeline:
- 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 then execute that pipeline to train and then deploy a tuned LLM.
- Output and study safety scores to responsibly monitor and filter your LLM application’s behavior.
- Try out the tuned and deployed LLM yourself in the classroom!
- Tools you’ll practice with include BigQuery data warehouse, the open-source Kubeflow Pipelines, and Google Cloud.
Who should join?
Anyone who wants to learn to tune an LLM, and learn to work with and build an LLMOps pipeline.
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