ML Ops: Operationalizing Data Science
A Four-Step Approach to Realizing the Value of Data Science through ML Operations
What does it take to operationalize data science and machine learning (ML) models that are the catalyst to meet your business objectives? You may have data science and ML, but do you have model operations? Have you created the processes needed to get your models from the data scientists who first develop them all the way to the applications that make advanced analytics available to the business? Who else — line-of-business managers, application developers, data engineers, DevOps, IT — is involved in the model operations lifecycle in your company? Do you think about it as a lifecycle? All of that is ModelOps.
Model operations, or ModelOps, is the process of operationalizing data science by getting data science & ML models into production. The four main steps in the process — Build, Manage, Deploy/Integrate and Monitor — form a repeatable cycle for handling models as reusable software artifacts. ModelOps ensures that models continue to deliver value to the organization, even as underlying business and technical conditions change.
In this ebook, we’ll walk you through:
- Realizing the value of data science and machine learning by reducing friction throughout those pipelines and workflows
- Managing differently than traditional software application engineering
- Embedding these models in a business system that differs from the development environment
- Structuring data science and ML pipelines in four steps: Build, Manage, Deploy/Integrate, and Monitor