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.
Today Enterprise IT does not question the value of containerized applications anymore. Given the move to adopting DevOps and cloud native architectures, it is critical to leverage container capabilities in order to enable digital transformation. Google’s Kubernetes (K8s), an open source container orchestration system, has become the de facto standard — and the key enabler — for cloud native applications, and the way they are architected, composed, deployed, and managed.
kube-proxy is a key component of any Kubernetes deployment. Its role is to load-balance traffic that is destined for services (via cluster IPs and node ports) to the correct backend pods. Kube-proxy can run in one of three modes, each implemented with different data plane technologies: userspace, iptables, or IPVS. The userspace mode is very old, slow, and definitely not recommended! But how should you weigh up whether to go with iptables or IPVS mode?
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.
Continuing the trend from last year, in 2019 we see more organizations riding the wave of Serverless and Kubernetes, and many are starting to see tangible results. The widespread adoption of these technologies, however, has only just begun. Below, we examine five trends in serverless that are sure to impact the way organizations develop and deliver software for years to come.
New technologies often require changes in security practices. What is remarkable about containers and Kubernetes, is that they also provide the potential for enhancing and improve existing security practices. In this post, I will share a model that we use at Nirmata to help customers understand security concerns and plan Kubernetes implementations that are secure.
More developers are keen on practices in terms of how they modernize monolith application into microservices easier, quicker, and smoothly. There are many microservices development frameworks such as Spring Boot and Linux container, container orchestration tools make it faster for your Microservices journey.
Serverless computing is all the rage at the moment, and why wouldn’t it be? The idea of deploying code without having to worry about anything like servers, or that pesky infrastructure everyone complains about seems pretty appealing. If you’ve ever used AWS lamdba or one of its related cousins, you’ll be able to see the freedom that triggering functions on events brings you.
