DZone

Building machine learning (ML) applications is a complex and iterative process. As more companies realize the untapped potential of unstructured data, the demand for AI-powered data processing and analytics will continue to rise. Without effective machine learning operations, or MLOps, most ML application investments will wither on the vine. Research has found that as little as 5% of the AI adoptions companies plan to deploy actually reach deployment. Many organizations incur “model debt,” where changes in market conditions, and failure to adapt to them, result in unrealized investments in models that linger unrefreshed (or worse, never get deployed at all).

This article explains MLOps, a systemic approach to AI model life cycle management, and how the open-source vector data management platform Milvus can be used to operationalize AI at scale.

Source: DZone