AI-Driven Services: The Invaluable Enterprise Asset
Creating real value from data means building - and maintaining - a spectrum of AI-driven applications and services that run as a core part of the business.
Learn MoreOnce an organization has the ability to quickly operationalize data projects and moves from a handful to hundreds (or thousands) of machine learning models in production, the question of maintenance and management arise. Enter: MLOps.
“Technology innovation leaders are keen to apply DevOps principles for AI and ML projects, but they often struggle with architecting a solution for automating end-to-end ML pipelines across data preparation, model building, deployment and production due to lack of process and tooling know-how.”
– Gartner, Accelerate Your Machine Learning and Artificial Intelligence Journey Using These DevOps Best Practices, 12 November 2019, Arun Chandrasekaran and Farhan Choudhary
MLOps help ensure that deployed models are well maintained, performing as expected, and not having any adverse effects on the business. This role is crucial in protecting the business from risks due to models that drift over time or that are deployed but unmaintained or unmonitored. At a time when issues like responsibility and bias are at the forefront, MLOps becomes even more important to close the feedback loop between operationalized models and their impact.
The age of AI presents additional risks across the enterprise that require a tighter — yet more flexible — governance structure.
Read moreCreating real value from data means building - and maintaining - a spectrum of AI-driven applications and services that run as a core part of the business.
Learn MoreThe ability to efficiently operationalize data projects is what separates the average company from the truly data-powered one.
Learn MorePut models in production with Dataiku's built-in API Deployer, making high availability and scalable deployments easy.
Learn MoreMonitor the behavior and overall functional health of Dataiku to ensure production readiness and optimize resource allocation.
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