Machine Learning At Speed: Operationalizing ML For Real-Time Data Streams

From home intrusion detection, to self-driving cars, to keeping data center operations healthy, Machine Learning (ML) has become one of the hottest topics in software engineering today. While much of the focus has been on the actual creation of the algorithms used in ML, the less-talked-about challenge is how to serve these models in production, often utilizing real-time streaming data. The traditional approach to model serving is to treat the model as code, which means that ML implementation has to be continually adapted for model serving. As the amount of machine learning tools and techniques grows, the efficiency of such an approach is becoming more questionable. Additionally, Machine Learning and model serving are driven by very different quality of service requirements; while machine learning is typically batch, dealing with scalability and processing power, model serving is mostly concerned with performance and stability.

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This article is related to

machine learning,ai,tensorflow,akka streams,data streaming