AI-300: Azure Machine Learning Operations Engineer Exam Prep

Deploy an azure machine learning workspace with its underlying resources in a managed resource group, then explore the studio and connections to build and manage models.

Compare compute targets in an Azure Machine Learning workspace, from compute instance and cluster to serverless spark and AKS, and learn when to deploy real-time endpoints or data processing.

Discover how to create and manage compute targets in the Azure ML workspace, including a CPU-based managed compute instance (DS11 v2) for notebooks, with idle shutdown, security, and tagging.

Compare Azure ML SDK v1 and v2, noting that v2 has higher abstraction, ML table assets, MLflow integration, and YAML pipelines for easier model training and deployment.

Compare Spark DataFrames and Pandas DataFrames to clarify when to use each in the machine learning lifecycle: Pandas for in-memory exploratory data analysis and visualization, Spark for distributed pipelines.

Learn linear and shrinkage regression algorithms, including linear, ridge, and lasso, plus logistic regression for binary classification, and how hyperparameters and MLflow enable penalized, accurate models.

Use the Azure Machine Learning Pipeline Designer to build an automobile price prediction model with linear regression, including data prep, a 70/30 split, training, scoring, and evaluation.

Create a real-time inference pipeline from an automobile price prediction model, deploy to an Azure container instance, and expose a real-time web service endpoint to serve predictions via api calls.

Explore real-time inferencing by calling a model behind an Azure container instance with a curl POST to the REST API endpoint, using keys, and viewing predictions and logs.

Explore the anatomy of the machine learning model development lifecycle in Azure Machine Learning workspace through experiments, jobs, and runs, with metrics, parameters, and model registry.

Learn to use confusion matrix and ROC curve to evaluate binary classification models. Explore recall, precision, accuracy, and F1 score, and understand area under the curve for stakeholder clarity.

Explore automated machine learning in Azure Machine Learning to identify the best diabetes prediction model by training multiple algorithms and deploying the winning ensemble to endpoints.

Perform hands-on lab tasks with mlflow to track experiments, log iteration and regularization hyperparameters, log accuracy, precision, recall metrics, and register the best diabetes logistic regression model in Azure ML.

Master hyperparameter tuning with Optuna and MLflow in Azure ML, using a diabetes dataset to optimize a decision tree by tuning depth and bin size for accuracy.

Build and execute an Azure machine learning pipeline by preparing data with prep_diabetes.py, training with train_diabetes.py, and logging metrics like accuracy and ROC AUC with MLflow.

Explore the principles of responsible AI—fairness, inclusiveness, reliability and safety, privacy and security, transparency, and accountability—and learn practical guardrails for trustworthy AI systems.

Explore responsible AI metrics like error analysis, cohort and error heat maps, and explanations of feature importance, counterfactuals, and causal analysis for interpretable model evaluation.

Explore real-time, batch, and web service endpoints in Azure Machine Learning, highlighting low-latency inference, high-volume batch predictions, and secured deployments behind ACI, AKS, and API gateways.

Master A/B testing and safe rollback in Azure Machine Learning workspace by routing traffic between model deployments behind a single endpoint and using model versioning to roll back when needed.

Explore how AI agents and compound AI systems extend large language models with external tools and APIs, enabling dynamic, autonomous planning and execution to automate business processes for ROI.

Learn about Azure Machine Learning deployment types for large language models, including global and data zone options, batch and provisioned throughput, and their data residency implications.

Build a simple named entity recognition PromFlow microservice in Azure Machine Learning using prompt engineering and Python scripting to extract entities.

Discover how retrieval augmented generation grounds large language model responses with vector embeddings and an augmented prompt, using Azure AI Search to keep enterprise data up to date.

Explore how Azure AI Search enables retrieval augmented generation and acts as an AI-enabled data store. Indexing Azure Blob Storage data supports AI enrichment with text and image analysis.

Run an automated evaluation of a rag PromFlow pipeline to assess groundedness using a downloaded grounding evaluation dataset, GPT-4.0, and a dedicated groundedness metric.

Master prompt engineering techniques, including chain of thoughts prompting, zero short prompting, and few short prompting. Apply best practices like clear syntax, task breakdown, and delimiters to improve LLM outputs.

Explore fixed size with overlap, semantic, recursive, adaptive, and context enriched chunking strategies for preparing documents for a vector search index and retrieval augmented generation pipelines.

Master vector search, keyword search, hybrid search, and semantic re-ranking in Azure AI Search, and understand HNSW vector arrangements and the retrieval pipeline.