Designing and deploying Machine Learning at scale is challenging, no matter the size of your team. Data Scientists are simply not trained in the often overwhelmingly complex discipline of deployment or turning their models into scalable applications. The intricacies of load balancing, event handling, and container management are a segment of the Machine Learning pipeline in of themselves, and there’s no straightforward playbook for how to make them work together. If you want to see meaningful ROI on your Machine Learning investments and build a competitive advantage this year, you’ll first need to solve this last mile deployment problem.
Deploying Machine Learning at Scale with Server less Micro services
