AI and Machine Learning
Azure MLOps, DataOps, Azure DevOps, Azure Databricks, Feature Stores
The customer builds advanced patient engagement, financial management, and population health technology solutions that enable retail pharmacies to improve their patients’ health while ensuring the long-term health of their business.
The healthcare organization offers the industry’s most comprehensive suite of SaaS technology solutions to transform and thrive in the new era of digital-driven healthcare. They wanted to build an AI-powered organization and move cloud workloads for AI from AWS to Azure to gain cost-efficiencies.
The organization wanted to develop Machine Learning models that enable them from the development to production with the right blend of architecture tools, infrastructure, feature store for CI/CD, and build a robust and scalable environment.
- Identify the right architecture for CI/CD integrations for MLOps and DataOps pipelines for production migration deployments and scale the ML models
- How to build Azure MLOps end-to-end CI/CD ML pipeline with DataOps and MlOps integrated with AI?
- How Azure Databricks can be integrated with Azure DevOps and write data to Databricks MLFlow/features?
- Deploying Databricks Feature stores & Azure ML
WinWire recommended leveraging Azure DevOps, Azure Databricks, Azure Machine Learning for data pipelines, and Databricks Feature factory store as architecture components for CI/CD. Standard methods couldn’t achieve the desired end-state, so WinWire’s AI team extended the functionality of open-source tooling to overcome the technical issues.
WinWire’s AI team helped the business gain a seamless transition and enabled software delivery acceleration with CI/CD principles and best practices. Azure DataOps and MLOps, an end-to-end life cycle management of an ML and Data pipeline, addressed the challenges with:
- Code management, data management, model building, model management
- Model profiling, governance, model deployment, data capture, drift monitor
WinWire AI Team conducted Azure DataOps, MLOps and ML sessions for development teams on Advanced topics for development to production, highlighting the entire ML model lifecycle.
- Optimized Architecture for AI/ML Deployments keeping AI principles & best practices and reduced TCO for the data sciences team
- Increased operational efficiency & improved productivity in AI/ML modeling and deployment
- Improved data scientist’s productivity with Databricks on Azure & facilitated competitive differentiation and customer satisfaction