Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.

Demo repository implementing an end-to-end MLOps workflow on Databricks, using Azure DevOps for CICD orchestration. Project derived from dbx basic python template

Project for e2e mlops using terraform, azure pipelines with wind output prediction

E2E MLOps on Azure

End 2 End ML Ops with Azure ML and GitHub Actions

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Demo repository implementing an end-to-end MLOps workflow on Databricks, using Azure DevOps for CICD orchestration. Project derived from dbx basic python template Resources

[👩‍🏫] In this repository I'll show you how to use Azure Databricks for development and training machine learning models, and build a MLOps pipeline to serving them with CI/CD process.

MLOps using Azure ML service with the Python SDK and Databricks for model training

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Project for e2e mlops using terraform, azure pipelines with wind output prediction structured as microservices