https://github.com/d-kleine/az_ml-engineering

ML Engineering with MS Azure
https://github.com/d-kleine/az_ml-engineering

automl azure hyperdrive ml-engineering mlops

Last synced: 17 days ago
JSON representation

ML Engineering with MS Azure

• Host: GitHub
• URL: https://github.com/d-kleine/az_ml-engineering
• Owner: d-kleine
• Created: 2023-10-12T17:16:49.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-26T13:08:50.000Z (10 months ago)
• Last Synced: 2024-11-23T14:12:39.387Z (30 days ago)
• Topics: automl, azure, hyperdrive, ml-engineering, mlops
• Language: Jupyter Notebook
• Homepage:
• Size: 19 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Codeowners: CODEOWNERS

Awesome Lists containing this project

README

        
# Machine Learning Engineering with Microsoft Azure

![Azure Logo](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Microsoft_Azure_Logo.svg/1280px-Microsoft_Azure_Logo.svg.png)

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.

## Program structure

### Azure Machine Learning

- 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](https://github.com/d-kleine/AZ_ML-Engineering/tree/main/project1_Optimizing-an-ML-Pipeline)

### Operationalizing Machine Learning

- 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)](https://github.com/d-kleine/AZ_ML-Engineering/tree/main/project2_Operationalizing-ML)

### Capstone project

* Combining all skills acquired in this program for a self-choosen ML project

  → [Capstone project: Heart Failure Prediction with AzureML](https://github.com/d-kleine/AZ_ML-Engineering/tree/main/project3_Capstone)

## Skills

- **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.