This repository contains the Porto Seguro’s Safe Driver Prediction project, which Predict if a driver will file an insurance claim next year. The complete project was implemented using Azure Machine Learning and Azure DevOps.

Machine Learning Operations (MLOps) is based on DevOps principles and practices that increase the efficiency of workflows.

This repository contains codes and guidelines for configuring the MLOps workflow with Azure as shown below:

Azure Machine Learning provides the following MLOps capabilities:

• Machine Learning pipelines allow you to define repeatable and reusable steps for your data preparation, training, and scoring processes.
• Create reusable software environments for training and deploying models.
• Register, package, and deploy models from anywhere. You can also track associated metadata required to use the model.
• Capture the governance data for the end-to-end ML lifecycle. The logged information can include who is publishing models, why changes were made, and when models were deployed or used in production.
• Notify and alert on events in the ML lifecycle. For example, experiment completion, model registration, model deployment, and data drift detection.
• Monitor ML applications for operational and ML-related issues. Compare model inputs between training and inference, explore model-specific metrics, and provide monitoring and alerts on your ML infrastructure.
• Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines. Using pipelines allows you to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services.