9 key tasks that CTOs must fulfill to build an enterprise anomaly detection system.

Regardless of which machine learning methods one uses to perform anomaly detection, analysts usually run into trouble when it comes to deploying such methods in an enterprise business environment. Resource allocation and usage demands, as well as latency, performance, and scalability requirements, are often underestimated. This can create major issues and complexities that data scientists and data engineers must deal with, when designing an anomaly detection pipeline.

In Moviri Analytics we strongly believe in a complete approach, combining a solid foundation in data engineering, big data technologies, and the leading-edge technologies in machine learning and MLOps, with an enterprise-focused approach.

In this paper, we offer a practical, high-level blueprint that CTOs can follow to make sure anomaly detection systems fulfill their innovation and value-creation promise.


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icon moviri  What is anomaly detection with machine learning and how it can be useful across business companies.

 icon moviri The 3 main stages and the 9 distinct critical steps to set a blueprint to unlock the full value of anomaly detection.