Develop a comprehensive understanding of machine learning models, data privacy safeguards, and effective end-to-end management of the machine learning lifecycle at scale using Azure Machine Learning's MLOps capabilities.

• Understanding the rationale for cloud-based machine learning.

• Efficiently utilizing workspaces and AzureML Studio.

• Integrating third-party and open datasets into machine learning pipelines.

• Managing pipelines and leveraging hyperparameters for improved prediction accuracy.

• Programmatically creating and managing pipelines using the Azure ML SDK.

• Automating machine learning processes with Hyperparameter Tuning and AutoML.

  → Project: Optimizing an ML Pipeline

• Authorizing operations for machine learning.

• Deploying machine learning models in Azure.

• Consuming and load-testing deployed services and endpoints.

• Creating batch inference pipelines and publishing them.

• Applying DevOps concepts for model deployment.

• Configuring and deploying a cloud-based machine learning production model using Azure.

  → Project: Operationalizing-ML (MLOps)

• Combining all skills acquired in this program for a self-choosen ML project → Capstone project: Heart Failure Prediction with AzureML

• Azure Machine Learning: Azure ML platform, Azure ML pipelines, Model interpretation, Azure ML SDK, Hyperparameter tuning.
• Machine Learning Operations: Model deployment with Azure, Kubernetes security, Deployment testing, Docker, Model evaluation.