How D2iQ Kaptain Works: A Brief Demonstration
See how D2iQ Kaptain helps data scientists and operations teams speed ML models to production.
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Introducing Kaptain, The Cloud Native End-to-End ML Platform
87% of AI initiatives never make it to production but remain stuck as prototypes or one-off research projects. Sustained positive returns on AI investments elude most organizations. In fact, 55% or organizations have not deployed a single machine learning model to production. And for those that have, it can easily take up to 3 months to do so. All this changes with D2iQ Kaptain.
AI Chihuahua: Why Machine Learning Is Dogged By Failure and Delays
A canine commerce conundrum reveals why Machine Learning is dogged by failure and delays.
Kubernetes Master Class - Thanos and Istio
Rancher simplifies the deployment and management of monitoring (Prometheus) and observability (Istio) on a cluster to cluster basis. Each of these tools have extensions that allow for global view and global access. With the recent introduction of Fleet, Rancher 2.5 has reduced the barrier to entry for these configurations, making them available to organizations running at any scale.
What is a Docker Container?
The rapid pace of updates and upgrades to operating systems, software frameworks, libraries, programming language versions – a boon to the future of fast-paced software development, has also come to slightly bite us in the back because of having to manage these very many dependencies with their different versions across different environments.
Publishing a Python package
For many software engineers and developers, using standard libraries or built-in objects is just not enough. To save time and increase efficiency, most developers build on work done by others. Whatever the coding problem, there is likely another programmer who has already created a solution for it. There is usually no need to repeat the problem-solving process. This principle is known as Do not Repeat Yourself or DRY.
Introduction to on-call schedules
An on-call schedule tells you and everyone in the team who will be the first responder when an issue happens in production. The on-call team member is responsible for investigating the issue, either fixing the issue herself or adding other people who can help fix it. Having an on-call schedule is important for building reliable systems because making someone responsible for production issues makes sure that they're not ignored.
How to deal with alert noise
Adding alerts across your monitoring tools is taking a proactive approach to reliability. But if there are too many alerts, then it can become counterproductive because team members will start ignoring alerts or remove the alerting altogether. Which is why you need a systematic approach to adding alerts and dealing with them.
Bridging Data and DevOps: Is Your DevOps Incomplete?
DevOps has great potential; when it’s done right, it offers organizations improved collaboration, quick innovation, better quality, and shorter delivery cycles. But the reality is that some businesses don’t carry the DevOps process through to the data layer.
Java Artifacts Just Got Better: jpackage is Production Ready in Java 16
If you shudder thinking about compilation for different platforms, I know the feeling. One of the Java promises, the WORA (Write Once, Run Anywhere) principle, while revolutionizing platform independence, it stopped short of one more step – to be able to deploy anywhere. Personally, I think that WORADA sounds awesome, but I guess before Docker it didn’t occur to people that eliminating “works on my machine” is as simple as shipping your machine.