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DevOps Trends in [2019]

DevOps solution is recognized more in the software development business, and it is growing during the technical progress. DevOps Trends & Solutions in 2019.

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DevOps Trends

Today, DevOps solution is recognized more in the software development business, and it is growing during the technical progress.

Like any other modern concept, you might get confused and seldom learn different views about what DevOps solution is. What is DevOps? Let’s go through various definitions and trends!

DevOps service has grown a long time since many of us assumed it was just a buzzword. But as now we recognize that is a myth.

DevOps has evolved as the main focus and has been improving the world of software for the last several ages. Specialists say that DevOps services are going to be the mainstream and its reputation is going to attain its top.

DevOps Trends in 2019

1. A Big Focus Towards Automation:

Automation is always an advanced focus for any business. Whether it’s fasting up to the pipeline with tools, giving excellent response rates for annoyed clients with a chatbot, or better record management with the following and logging process, automation is doing it all.

Companies that execute automation as a piece of their large DevOps applications can use more time on the information that directly affects their company, instead of spending it on-time spending methods that could be easily automated.

When DevOps, I mean development and operations both have extra time to use their talents and knowledge to achieve changes and innovations as they don’t have to spend it on ordinary jobs, everyone profits.

Automation has grown a fundamental enabler of DevOps solutions, and it would be extremely tough to think one another without each other.

If DevOps recognizes the software development procedure as a unique pipeline, then automation is a high-pressure pump that forces products over it at a much quicker rate and with much higher security than would be achievable otherwise.

2. Software Development Team Will Increase Visibility Into Processes:

Nowadays, more and more companies are depending upon the DevOps solutions, and that’s why they’ll also seem for methods to improve perceptibility into practices and scale its influence.

Using governance characteristics in DevOps tools like automated tracking and recording on a compound of metrics, organizations can view a clear report like what modifies, when, and what permits or licenses are required at each phase.

Also, the feedback system will be reduced and provide more progressive changes to the business rules which the applications support.

Such clarity will allow employees to evaluate what’s running with tools and processes and recognize states that require be modifying or updating.

3. Increase Use of Microservices Architecture:

DevOps services and microservices are now applying together. Microservices architecture serves businesses to do deployments and add distinct points quickly.

Organizations are required to go to a microservices architecture to improve their runtime and effective performance. Don’t just copy others as they used it, understand yourself, and explain why you should use microservices architecture.

Microservices produce added productivity to DevOps by adopting a standard toolset, which is utilized for both development and operations. That standard toolset builds general technology, as well as methods for necessities, provinces, and difficulties.

Which will make it more comfortable for Devs and Ops to go with one another, letting those things to run together on challenges, and successfully resolve a build configuration or create the script? DevOps services and microservices work great when implemented concurrently.

4. Kubernetes will become the Power For DevOps:

With the use of Kubernetes, developers can distribute their software and dependencies quickly with IT operations. It reduces the workload and explains the disputes between diverse situations.

Kubernetes monitoring is efficient with the Kubernetes dashboard as it offers a real-time overview of applications running over a cluster. Additionally, the dashboard presents the Kubernetes resources in the cluster and resolves errors. You can easily create and modify the Kubernetes resources.

Container Orchestration takes IT operations and developers closer to each other, which will make it more trouble-free for the developers to cooperate efficiently and effectively with each other.

Kubernetes offer the tool for the developers that react to the consumer requirements while depending on the cloud for the responsibility of working applications. It is achieved by reducing the manual works that are associated with expanding and estimating containerized claims so that it allows running software more probably when migrated from one environment to another.

5. Big Data and DevOps

For big data, there is a significant element, which is this performance features. We think that execution requires being a great player in DevOps for big data.

Collecting the data about how things are precisely working and giving feedback to advanced elements of the DevOps chain is essential for big data. DevOps solution can give valuable feedback loops back like they have to change their algorithm or not, the part placed at the proper pale or not, requirements of resources, etc. which are very important for big data. That is our essential opinion.

DevOps is growing every year, and there are so many essential things being focused on the betterment of processes, which can cause the operation of the DevOps even more powerful and helpful. We are hoping for every company to be a DevOps services company by 2020.

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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

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Will AI render Software Developers Obsolete

Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

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