The technology, which is included in our lives, has gained a rapid acceleration recently. While the coronavirus outbreak turned into pandemic and its affect will be continued in the foreseeable future, perhaps the insights that we will incorporate into our lives many years later brought into our lives.
There is only one thing to do to minimize the severe adaptation process of companies. Include these new technologies in their configurations as soon as possible;
Artificial intelligence, which has been in science fiction worlds for a long time, has become the key point of the future and possible epidemics while entering our lives quietly and secretly in the past.
Although it is still at a very early stage, it leads a revolution comparable to that produced by the Internet.
However, you need to have your organization embedded in an artificial intelligence infrastructure and have artificial intelligence experts in your company structure.
To get training in the field of artificial intelligence, check out our virtual trainings.
Machine learning (ML) is a type of artificial intelligence that allows software applications to be more accurate in predicting results without explicitly programming it. Machine learning algorithms use historical data as input to estimate new output values.
Recommendation engines are a common use case for machine learning. Other popular uses are fraud detection, spam filtering, malware threat detection, business process automation (BPA), and predictive sustainability.
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Deep Learning is a subfield of machine learning related to algorithms inspired by the structure and function of the brain called artificial neural networks.
Deep learning algorithms try to take advantage of the unknown structure in the input distribution to discover good representations, usually at multiple levels, with higher-level learned features defined in terms of low-level features.
Deep learning methods aim to learn the structure with the features of the higher level hierarchy created by the combination of lower level features. Automatic learning features at multiple levels of abstraction allow a system to learn complex functions that map input directly from data, regardless of completely man-made features.
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With the rapid evolution of today, we need more functional and defensive structures. We can say the best and most reliable of these is Docker.
Docker has many moving parts and its security is more complicated than preserving legacy distribution technologies. It requires a holistic approach from network and application firewalls to container runtime defense to host protection.
Securing the Docker container is no different from the security of other containers. It requires an all-inclusive approach that provides security anywhere from the host to the network and everything in between. Due to its moving parts, securing containers is difficult for many organizations and requires more than primitive caution.
Enroll for our Docker training today and learn more!
Kubernetes is a powerful open source system originally developed by Google to manage inclusive applications in a clustered environment. It aims to provide better ways to manage related, distributed components and services across various infrastructures.
Kubernetes is a system for running and coordinating containerized applications in a cluster of machines at a basic level. It is a platform designed to fully manage the lifecycle of inclusive applications and services using methods that provide predictability, scalability and high availability.
As a Kubernetes user, you can define how your applications should work and how they can interact with other applications or the outside world. You can scale your services up or down, perform elegant updates, and switch between different versions of your apps to test features or undo problematic deployments. Kubernetes provides interfaces and combinable platform principles that enable you to define and manage your applications with a high degree of flexibility, power and reliability.
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