Model Deployment for Data Scientists Using TensorFlow: Part 1 - Nightfall AI

In the world of machine learning, model deployment is a crucial piece of the puzzle. While data scientists excel at other parts of the pipeline, deploying machine learning models tends to fall under the umbrella of software engineering or IT operations. And for good reason--successful deployments require a myriad of complex tasks, including building infrastructure, implementing APIs, load balancing, and integrating with data pipelines. We'll briefly walk you through a basic model deployment example by picking out tools and planning out an approach to construct a simple sentiment classification model. By the end of this post you will have the tools to serve your deep learning (DL) models via an API.