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- Get started with language models in Azure Databricks
In this module, you learn how to:
- Describe Generative AI.
- Describe Large Language Models (LLMs).
- Identify key components of LLM applications.
- Use LLMs for Natural Language Processing (NLP) tasks.
- Implement Retrieval Augmented Generation (RAG) with Azure Databricks
In this module, you learn how to:
- Set up a RAG workflow.
- Prepare your data for RAG.
- Retrieve relevant documents with vector search.
- Improve model accuracy by reranking your search results.
- Implement multi-stage reasoning in Azure Databricks
In this module, you learn how to:
- Identify the need for multi-stage reasoning systems.
- Describe a multi-stage reasoning workflow.
- Implement multi-stage reasoning with libraries like LangChain, LlamaIndex, Haystack, and the DSPy framework.
- Fine-tune language models with Azure Databricks
In this module, you learn how to:
- Understand when to use fine-tuning.
- Prepare your data for fine-tuning.
- Fine-tune an Azure OpenAI model.
- Evaluate language models with Azure Databricks
In this module, you learn how to:
- Evaluate LLM evaluation models
- Describe the relationship between LLM evaluation and AI system evaluation
- Describe standard LLM evaluation metrics like accuracy, perplexity, and toxicity
- Describe LLM-as-a-judge for evaluation
- Review responsible AI principles for language models in in Azure Databricks
In this module, you learn how to:
- Describe the responsible AI principles for implementation of language models.
- Identify the ethical considerations for language models.
- Mitigate the risks associated with language models.
- Implement key security tooling for language models.
- Implement LLMOps in Azure Databricks
In this module, you learn how to:
- Describe the LLM lifecycle overview.
- Identify the model deployment option that best fits your needs.
- Use MLflow and Unity Catalog to implement LLMops.
