Scaling Machine Learning in Organizations: A White Paper

What is MLOps?

Machine learning (ML) has become an essential tool for businesses to extract insights and patterns from data. However, moving from an experimental model to a value-creating model in a robust production setup is a challenge. We've created a white paper which introduces MLOps and how it enables organizations to get their enterprise machine learning models successfully deployed to production, bringing real value to their businesses.

MLOps is a core function of ML engineering that streamlines the process of taking ML models to production and maintaining and monitoring them. It inherits from the principles of DevOps for traditional software development, encompassing best practices for the lifecycle management of machine learning.

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Framework for MLOps

twoday's framework for MLOps provides a reusable and structured way of running ML pipelines on Azure that leverages the full list of MLOps principles. The framework supports both Azure Architecture and ML Projects and includes:

Consistency and Speed:

The framework deploys and links the needed services to create an ML ecosystem on Azure, relying on infrastructure-as-code and pre-configured CI/CD pipelines.

Security:

Security is built into the framework, and all secret values are stored in a secure location while Azure’s role-based access control caters for access for individual users and cloud services.streamlinehq-rating-star-social-medias-rewards-rating

More than Azure ML:

The backbone of the framework is the Azure ML workspace, but it also supports other Azure services such as SQL servers, Databricks workspaces, and Azure Data Factory.

Best Practices:

The framework enforces best practices for important aspects of development and deployment, including classical DevOps practices of CI/CD deployment, version control and branching strategies, testing of code, orchestration, and infrastructure provisioning.

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Implementing machine learning applications successfully in businesses requires more effort and diverse skill sets than one realizes at the start of the ML journey. twoday's MLOps framework is designed to be the foundation for an end-to-end solution on Azure, enabling reproducibility, standardization, and automation of ML pipelines.

Fill out the form below to download the original white paper and learn more about how MLOps can help your organization scale its machine learning projects and deploy multiple models to production.