We need a practical and scalable approach to understand the cause-effect relationship between data sources and events across complex infrastructure of VMs, containers, networks, micro-services, regions, etc. Machine learning is particularly useful for such problems where we need to identify “what changed”, since machine learning algorithms can easily analyze existing data to understand the patterns, thus making easier to recognize the cause. This is known as unsupervised learning, where the algorithm learns from the experience and identifies similar patterns when they come along again.
Enterprises need to adopt a new approach to software development and digital innovation. At Velotio, we are helping customers to modernize and transform their business with all of the approaches and best practices listed here in this blog. We talk in detail about how to achieve agility, cloud native development, DevOps maturity, micro-services adoption, digital transformation and build intelligent applications using data science in a secure environment.
Containerized applications are becoming more popular with each passing year. A reason for this rise in popularity could be the pivotal role that they play in Continuous Delivery by enabling fast and automated deployment of software services. Security still remains the major concern mainly because of the way container images are being used. This blog provides an answer to the below concerns:
- There are so many docker images readily available on dockerhub, but are you sure the one that you are using is not injecting any vulnerability into your environment?
- Do you know where your containers come from?
- Are your developers downloading container images and libraries from unknown and potentially harmful sources?
- Do the containers use third party library code that is obsolete or vulnerable?