Operations | Monitoring | ITSM | DevOps | Cloud

As industries shift to a microservices approach of deploying applications using containers, data scientists can reap the benefits. Data Scientists use specific frameworks and operating systems that can often conflict with the requirements of a production system. This has led to many clashes between IT and R&D departments. IT is not going to change the OS to meet the needs of a model that needs a specific framework that won’t run on RHEL 7.2.

A common sentiment among our prospects after they see our demo for the first time is: “That’s it? It can’t be that simple!”. The truth is – yes it can be, and it should be. ML and AI should make IT Ops simpler, and a big part of that is usability. If your ML & AI powered IT Ops tools take months to set up and weeks to learn, and then don’t provide a substantially improved user experience, you’re obviously using the wrong tools.

This post demonstrates a *basic* example of how to build a deep learning model with Keras, serve it as REST API with Flask, and deploy it using Docker and Kubernetes. This is NOT a robust, production example. This is a quick guide for anyone out there who has heard about Kubernetes but hasn’t tried it out yet. To that end, I use Google Cloud for every step of this process.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we’ll talk about why incorporating AI into your UEM strategy may be inevitable.