Why Operationalizing Machine Learning Needs A Different ApproachBy Boris Lublinsky, Principal Architect at Lightbend
Machine learning is certainly one of the hottest topics in software engineering today, but one aspect of this field demands more attention: how to serve models that have been trained. In conjunction with our partner O'Reilly, Lightbend is pleased to be able to offer you this expert guide to Machine Learning.
In a typical scenario, two different groups in an organiztion are responsible for model training and model serving. Data scientists often introduce their own machine-learning tools, causing software engineers to create complementary model-serving frameworks to keep pace. It’s not a very efficient system.
In this practical report, Boris Lublinsky, Principal Architect at Lightbend, demonstrates a more standardized approach to model serving and model scoring, introducing architectures for serving models in real time as part of input stream processing. You’ll explore:
If you are a developer or architect looking to learn more, consider our Fast Data Platform Technical Overview or Fast Data Architectures for Streaming Applications, by Dean Wampler (VP of Fast Data Engineering at Lightbend). If you are a team lead or manager, review our Reactive Launch engagement and read some of our streaming and Fast Data success stories from enterprises like Credit Karma, Weight Watchers, Zalando, Swisscom and Intel:
If you feel like the time is right, contact a Lightbend representative to schedule a 20-min introductory chat with you and your team here:
