Journey to Real-Time Machine Learning

The journey to real-time machine learning adoption is complex, requiring deep changes within an organization. It typically starts with traditional analytics, followed by analytical ML, then operational ML, and finally real-time ML, each step building on the previous one as organizations mature in their use of machine learning. Real-time ML requires rapid deployment of models at scale, low-latency prediction, and the ability to consume real-time data sources, making it an aspirational objective for most enterprises. To achieve this, organizations need to develop data science expertise, bring in ML engineering expertise and processes, adopt MLOps tooling, and operationalize complex data pipelines with sub-second freshness.

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