MLOps in Azure using Python SDK — Part 1
Introduction to MLOps using AzureML SDK
Taking a Machine Learning project to production involves multiple components — Data Engineering, DevOps, and Machine Learning. The intersection of these components is MLOps. MLOps (Machine Learning + DevOps) is the process of taking a machine learning project to production — with the goal of automating and improving the quality of production models, while also focusing on business and regulatory requirements.
Key Phases in MLOps
- Data Gathering
- Data Analysis
- Data Transformation/Cleaning
- Model Training & Development
- Model Validation
- Deploying the Model
- Serving the Model as a web app or an API
- Model re-training
Another key feature in MLOps is that we will also want to track metrics across experiments — how did the model perform for example needs to be tracked across various hyperparameters so that we can choose the best model.