ML Ops with Azure Machine Learning Accelerator: 4-week implementation


Accelerate, manage and automate your machine learning lifecycle

Machine learning (ML) is a powerful way to generate insights and predictions from your data, but it also comes with many challenges and complexities. You need to manage your ML code, data, and artifacts, automate your ML workflows, track and compare your ML experiments and metrics, deploy and monitor your ML models, and collaborate and share your ML insights with your team and stakeholders.

All of these tasks require a robust and scalable ML Ops solution that can help you scale your AI initiatives and increase the efficiency, quality, and reliability of your ML solutions in a way that is secured, maintainable, governed and monitored.

Azure Machine Learning is a cloud-based platform that provides end-to-end capabilities for building, training, deploying, and managing ML models. Azure DevOps is a platform for collaborative software development and delivery that integrates with Azure technologies including Azure Machine Learning. Together with Azure DevOps, it allows you to implement best-practice ML Ops to:

• Manage your ML code, environments, data, and artifacts in a single repository using Azure Machine Learning, Azure DevOps and the open-source mlflow • Automate your ML workflows with Azure DevOps pipelines and Azure Machine Learning pipelines • Track and compare your ML experiments and metrics with Azure Machine Learning studio and mlflow • Deploy and monitor your ML models to various endpoints with Azure Machine Learning • Enforce quality and security standards with code reviews and pull requests


This unique 4-week engagement will include:

• A 3-hour workshop to assess your current ML environment and identify your ML Ops goals and challenges and desired future state • Designing a customized ML Ops solution and architecture to operationalise a selected ML use case based on your specific requirements • Implementing the ML Ops solution using Azure Machine Learning and Azure DevOps • Delivering an example model monitoring dashboard in Power BI • Validating the functionality and performance of the ML Ops solution

On completion, a comprehensive handover session will be conducted with the team and a Operations Guide detailing the configuration of the ML Ops solution as delivered will be provided.

*NOTE: All terms, conditions and pricing are subject to each engagement.