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Chapter 1: Design and prepare a machine learning solution-Design a machine learning solution-Identify the structure and format for datasets
Chapter 2: Design and prepare a machine learning solution-Design a machine learning solution-Determine the compute specifications for machine learning workload
Chapter 4: Design and prepare a machine learning solution-Create and manage resources in an Azure Machine Learning workspace-Create and manage a workspace
Chapter 5: Design and prepare a machine learning solution-Create and manage resources in an Azure Machine Learning workspace-Create and manage datastores
Chapter 6: Design and prepare a machine learning solution-Create and manage resources in an Azure Machine Learning workspace-Create and manage compute targets
Chapter 8: Design and prepare a machine learning solution-Create and manage assets in an Azure Machine Learning workspace-Create and manage data assets
Chapter 9: Design and prepare a machine learning solution-Create and manage assets in an Azure Machine Learning workspace-Create and manage environments
Chapter 10: Design and prepare a machine learning solution-Create and manage assets in an Azure Machine Learning workspace-Share assets across workspaces by using registries
Chapter 11: Explore data, and run experiments-Use automated machine learning to explore optimal models-Use automated machine learning for tabular data
Chapter 12: Explore data, and run experiments-Use automated machine learning to explore optimal models-Use automated machine learning for computer vision
Chapter 13: Explore data, and run experiments-Use automated machine learning to explore optimal models-Use automated machine learning for natural language processing
Chapter 14: Explore data, and run experiments-Use automated machine learning to explore optimal models-Select and understand training options, including preprocessing and algorithms
Chapter 15: Explore data, and run experiments-Use automated machine learning to explore optimal models-Evaluate an automated machine learning run, including responsible AI guidelines
Chapter 16: Explore data, and run experiments-Use notebooks for custom model training-Use the terminal to configure a compute instance
Chapter 17: Explore data, and run experiments-Use notebooks for custom model training-Access and wrangle data in notebooks
Chapter 18: Explore data, and run experiments-Use notebooks for custom model training-Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute
Chapter 19: Explore data, and run experiments-Use notebooks for custom model training-Retrieve features from a feature store to train a model
Chapter 21: Explore data, and run experiments-Use notebooks for custom model training-Evaluate a model, including responsible AI guidelines
Chapter 22: Explore data, and run experiments-Automate hyperparameter tuning-Select a sampling method
Chapter 23: Explore data, and run experiments-Automate hyperparameter tuning-Define the search space
Chapter 26: Train and deploy models-Run model training scripts-Consume data in a job
Chapter 30: Train and deploy models-Run model training scripts-Define parameters for a job
Chapter 34: Train and deploy models-Implement training pipelines-Create a pipeline
Chapter 36: Train and deploy models-Implement training pipelines-Run and schedule a pipeline
Chapter 38: Train and deploy models-Manage models-Define the signature in the MLmodel file
Chapter 39: Train and deploy models-Manage models-Package a feature retrieval specification with the model artifact
Chapter 42: Train and deploy models-Deploy a model-Configure settings for online deployment
Chapter 47: Train and deploy models-Deploy a model-Invoke the batch endpoint to start a batch scoring job
Chapter 48: Optimize language models for AI applications-Prepare for model optimization-Select and deploy a language model from the model catalog
Chapter 49: Optimize language models for AI applications-Prepare for model optimization-Compare language models using benchmarks
Chapter 50: Optimize language models for AI applications-Prepare for model optimization-Test a deployed language model in the playground
Chapter 51: Optimize language models for AI applications-Prepare for model optimization-Select an optimization approach
Chapter 52: Optimize language models for AI applications-Optimize through prompt engineering and Prompt flow-Test prompts with manual evaluation
Chapter 53: Optimize language models for AI applications-Optimize through prompt engineering and Prompt flow-Define and track prompt variants
Chapter 54: Optimize language models for AI applications-Optimize through prompt engineering and Prompt flow-Create prompt templates
Chapter 55: Optimize language models for AI applications-Optimize through prompt engineering and Prompt flow-Define chaining logic with the Prompt flow SDK
Chapter 56: Optimize language models for AI applications-Optimize through prompt engineering and Prompt flow-Use tracing to evaluate your flow
Chapter 57: Optimize language models for AI applications-Optimize through Retrieval Augmented Generation (RAG)-Prepare data for RAG, including cleaning, chunking, and embedding
Chapter 59: Optimize language models for AI applications-Optimize through Retrieval Augmented Generation (RAG)-Configure an Azure AI Search-based index store
Chapter 60: Optimize language models for AI applications-Optimize through Retrieval Augmented Generation (RAG)-Evaluate your RAG solution
Chapter 61: Optimize language models for AI applications-Optimize through fine-tuning-Prepare data for fine-tuning
Chapter 62: Optimize language models for AI applications-Optimize through fine-tuning-Select an appropriate base model
Chapter 63: Optimize language models for AI applications-Optimize through fine-tuning-Run a fine-tuning job
Chapter 64: Optimize language models for AI applications-Optimize through fine-tuning-Evaluate your fine-tuned model

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