MLOps Model Factory

Our customers build ML capabilities to improve operational efficiency and automation. Developers often face challenges in scaling these capabilities across an enterprise in a repeatable, reliable manner. Enterprises want to manage multiple ML Models along with their versions, lineage and trace. They want to deploy ML Models consistently across environments, optimize resource cost, automate repetitive tasks. They want a standardized framework across their suite of ML Models for better manageability and operations. The following solution helps software engineers and data scientists to use Azure ML and Azure DevOps to create these items:

• Repeatable ML experiments for multiple ML Models
• Reusable code across ML Models
• Scalable deployments of their ML Models

Business Problem

Traditional software development is not well suited to the unique requirements of machine learning models. The models can change rapidly as new data becomes available and as model performance evolves. Often, machine learning models are developed in a research environment and then moved to a production environment manually. Because models need to be maintained and updated in production, this manual method of deployment leads to:

• Inefficiencies in deployment and management.
• Limited reproducibility and transparency of ML models.
• Inhibited collaboration between data science and operations teams.
• Reduced ability to monitor and detect drift in ML models.

Solution overview

MLOps Model Factory provides a structured platform for generating, organizing, versioning, and tracking multiple ML models. It makes it easier to manage and keep track of models throughout their lifecycle. Implementing MLOps Model factory addresses the following challenges:

• Model management complexity
• Scalability
• Deployment consistency
• Collaboration
• Governance
• Experimentation
• Continuous improvement
• Cost optimization

It streamlines the management and deployment of multiple models. The factory improves efficiency, reliability, and agility across environments.

Value proposition

MLOps Model Factory provides help with:

• Enabling transparent and reproducible ML model training for many models.
• Automating model retraining and deployment to reduce human error and inefficiency.
• Simplifying the path from experimentation to deployment of a new model.
• Scaling Model experimentation for Data Scientists who define and develop ML models.
• Applying standards for governance and security of ML models.

Logical Architecture

The MLOps process the Model Factory follows is:

Implementation

The MLOps Model Factory implementation is built on Azure DevOps and Azure Machine Learning. It is possible to extend the code to execute in GitHub or GitLab by writing workflows for the same.

MLOps Model Factory provides a systematic approach to generating and managing machine learning models for a production environment. It ensures that models are built consistently, reliably, and efficiently across different environments and platforms. It enables organizations to scale their machine learning workflows by automating repetitive tasks, optimizing resource allocation, and streamlining the deployment process. It helps in efficiently managing computational resources and handling large-scale machine learning workloads.

Learn more

To learn more, refer to these articles:

• Case Study – Accelerating ML Model Development and Deployment on the edge using MLOps Model Factory Accelerator
• Understanding production AI building blocks
• MLOps: Model management, deployment, and monitoring with Azure Machine Learning
• Setting up MLOps with Azure DevOps
• Azure Machine Learning
• Azure Pipelines
• Create a service principal using the Azure portal
• Azure Machine Learning SDK v2