Curated articles, resources, tips and trends from the DevOps World.
Rafay Systems sponsored this post. Now more than ever, hybrid and multicloud deployments are quickly becoming key enterprise requirements. As Kubernetes adoption in an enterprise grows, effectively managing multicluster deployments becomes increasingly critical to application delivery.
Dynatrace sponsored this post. Developing reliable applications is more important than ever as organizations become increasingly dependent on digital services. Performance degradation and downtime can reduce revenue, increase customer churn and cause reputational damage.
Service mesh by definition is supposed to help reduce the complexity associated with Kubernetes.
InfluxData sponsored this post. Time-series databases like InfluxDB are databases that specialize in handling time-series data, which is data that is indexed by time.
The idea of distributing machine learning is not a new one. Google was one of the first to implement it on a large scale by training Android phones for performing keyboard autocompletion.
Security teams frequently struggle with the volume of alerts and issues they are tasked with daily. On average, most enterprises receive between 10,000 and 150,000 a day. Regardless of how large a security team may be, manually going through alerts like this is an almost impossible task.
Service Control Policies, or SCPs for short, are a big step forward in getting more centralized control over which AWS services are being used in your AWS Organization. So If you are in a position where you have responsibility for the AWS Cloud infrastructure, then you must read this post!
Software development has evolved into an incredibly complex machine, with several moving parts to keep track of. Teams get more extensive, and software systems become more complicated as time goes on.
This post in our Cloud Provider Comparisons series jumps into a space that’s super dynamic for cloud providers – artificial intelligence and machine learning.
There are a variety of technology stacks for Artificial intelligence (AI), Machine learning (ML) and data analytics applications. However, the ideal programming language for AI must be powerful, scalable and readable. All three conditions are met by the Python programming language.
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