Paul Welch

Happy New Year! I don’t normally make predictions for the new year. But, this year I’ve had a lightbulb moment. It took me a while to make a connection between these two seemingly independent trends. I’ve been following and working with them for a long time. The foundations of both DevOps and Machine Learning (ML) go back decades. The hype around them lately is deafening. Yet, I haven’t heard much about how well they go together. That’s about to change…

So you’ve trained your TensorFlow ML model, now what? (Part 3 - Using the model in other apps)

The first two posts in the series showed how to run a TensorFlow model, then how to deploy it to Kubernetes. Now that our image classification service is managed by a scheduler and scaled dynamically, the next step is to start using it. In this post, we’ll walk through an example Node.js single page app (SPA) that calls the service and displays the classification results.

So you’ve trained your TensorFlow ML model, now what? (Part 2 - Deploying to Kubernetes)

Part 1 showed how to run the open source pre-trained model Inception V3 as an image classification service. That’s all you need to do for a single classification instance, whether you run it on a server, your laptop or even a smart IoT device. But, what if you need to scale to handle a high volume of concurrent requests or you want multiple instances for resilency? For that, you’ll want to scale horizaontally with many instances. Then, a cluster managed by a resource scheduler like Kubernetes is a great way to go. Not only does it help with scalability, but it also enables deployment automation, improves manageability and will most likely result in better infrastructure utilization.