When done well, DevOps can remove inefficiencies by improving process and performance but only when clear outcomes are established. These improvements are transferable across a wide range of industries, with consistent benefits achievable for organisations of all sizes. So if you are looking for reasons why you should introduce DevOps,here are five benefits that help you make that decision. However, these benefits are all intrinsically linked, so there's little chance that all will immediately appear once your DevOps journey has begun.
Sidechains are a new concept in the blockchain industry and very well in the developing mode. It’s a new blockchain developed as a separate one attached with the original blockchain system. Well, the sidechain is attached or connected with the normal blockchain, and thus two blockchain networks are now available in a single system – main blockchain & sidechain. Connected via a 2-way passage, and the main objective of this new concept is to validate the transaction without affecting the whole system.
The technology support with AutoML and deep-learning is there now, where this is a 24hr curiosity for an engineer instead of a 7 figure high-risk 12–16-month science project. As these types of problems become low hanging fruit, we will see more job disruption. At first, the jobs will be augmented/validated, and then eventually they will be automated (except appraisals that are predicted to have issues e.g. on a ski resort, etc…). In the end, there is no reason why this wouldn’t be completely automated.
DevOps has come a long way in the last 10 years. From its humble beginnings, the mash-up between development and operations has grown into a major focus for businesses all over the world. Today, more than 70 per cent of companies claim to have adopted DevOps, and it's easy to see why. Sadly, most organisations are failing to hit numbers. Here's five key ways businesses are still getting it wrong - and what they can do about it.
While the open, decentralized internet is alive and well in the InternetOne layer, the InternetTwo layer has become highly centralized, dominated by a few huge companies. Blockchain technologies have the potential to address these serious internet problems by enabling the exchange of the critical data required to validate identities in a secure, decentralized manner without the need for a central platform or other intermediaries. Over time, blockchain-based applications could be used to coordinate the self-organizing activities of large numbers of individuals and institutions in a secure and decentralized manner, as was the case with the internet’s early objectives.
While DevOps is not natural, a supernatural result is achievable with the right cultural transformation supported by good tools that drive collaboration and automated flow between people and tools in the application delivery workflow. Implementing DevOps properly is challenging. DevOps depends on collaboration; smooth process flow from conception through deployment; and feedback between people, processes and technologies. It’s more natural for people and departments to hoard information and centralize control—traits that are contrary to DevOps best practices.
The IoT is still expanding exponentially. How will we manage and maintain the networks, data, clouds, and connections that govern and transmit this data? An IoT skills gap may be standing in our way. If your company is not yet feeling the pain of the IoT skills gap, take heart: you soon will. The following are some tips for lessening the damage to your business. The easiest way to deal with the IoT skills gap in your workforce is upskilling and retraining programs.
RPA offers retailers the opportunity to automate processes and data management today, while simultaneously positioning them for the future. Early adopters are already reaping the benefits. Those retailers still uncertain of the opportunities that RPA can bring will be assured by the fact that RPA can provide more opportunities than threats for retail workforces. It’s a no-brainer for businesses, too, as the benefits of RPA easily transcend headcount and cost reduction. It’s time for retailers to welcome the (software) robots.
Every business needs to stay up to date on and comply with the latest encryption and privacy laws. Failure to comply will result in fines that can range upwards of tens of millions of dollars. But which laws do you need to comply with, and what do you have to do? For the sake of this article, we’re just going to focus on the regulations and laws that require encryption or reference the protection of encrypted data. These regulations and laws are sometimes called data encryption laws, data privacy laws or data protection laws.
Machine learning is not new, but there is a new paradigm to do it, and maybe it’s the future of the field. Inside of the data fabric, we have new concepts like ontology, semantics, layers, knowledge-graph, etc; but all of those can improve the way we think about and do machine learning. In this paradigm, we discover hidden insights in the data fabric by using algorithms that are able to find those insights without being specifically programmed for that.
Machine learning (ML) has a surprise, too. One of the biggest misconceptions about ML deployment within organizations is comprehending the difficulty and the value. Integrating ML into your business workflows can be broken down into five activities. Optimizing an ML algorithm takes much less relative effort, but collecting data, building infrastructure, and integration each take much more work. The differences between expectations and reality are profound. Not every problem has an ML-powered solution, but many do, and even those that do not will benefit from this journey.
AI-based platforms are quantifying and getting value from you by gathering your contacts, your ideas, your skills, your free time, your shopping habits and even your very genes. The tradeoffs here are not so clear. Companies win big using your data. So, on top of worrying about whether or not AI will steal your job, you must also worry about AI stealing your time, information, ideas, relationships, and more. AI is breaking us down into our piece parts, and using them up.
SQL is a relational database management system used for storing, retrieving, updating and reading data from the database. As long as there is ‘data’ in data scientist, Structured Query Language, or see-quel, as we call it will remain an important part of it. You should understand that if you are crazy about data and playing with it, and want Data Science as your career choice, you should definitely learn SQL. Let us explore data science and its relationship with SQL.
As every enterprise is becoming more and more data-driven, it is key for the Board to realize that cyber security is becoming a central tenet both of its core business and of its social impact and governance strategies. This should the basis on which the cyber security imperative is cemented at Board level. Right where it always belonged. Here are Key factors for boards and executive management to consider in 2019 around cyber security and privacy.
We must recognize that artificial intelligence is a fluid term, whose definition changes with time. Therefore, we need to define what is the current context of AI. Can we consider anything that uses a machine learning algorithm as artificial intelligence? Should AI be limited to systems that employ neural networks and deep learning algorithms? Or maybe we should evaluate AI based on the cognitive behavior a system manifests, regardless of the underlying technology? If yes, what is the minimum level of cognitive accomplishment for a system to be considered AI?
There are many different basic sorting algorithms. Some perform faster and use less memory than others. Some are better suited to big data and some work better if the data are arranged in certain ways. Choosing which library and type of sorting algorithm to use can be tricky. Implementations change quickly. In this article, I’ll give you the lay of the land, provide tips to help you remember the methods and share the results of a speed test.
It is important to know which activation functions to use within your neural network. Be aware of the fact that you can use different activation functions at different layers. Most often the sigmoid function is used but often other functions can work much better. In this post you will learn the most common Activation Functions within Deep Learning and when you should use them. You will also discover why you mostly need to use non-linear activation functions.
Cyberattacks to disrupt the business are now ranked as the third-biggest threat, after phishing and malware. This comes as no surprise because distributed denial-of-service (DDoS) attacks, for instance, can trigger a major service interruption that will bring the business to a standstill. Outages have always been painful but given the trend toward moving workloads and applications off-premises, and operating revenue-critical platforms, business operations virtually come to a stop if the IP network collapses.
Generally, AR and VR are utilized in industry sectors such as finance, healthcare, construction, and retail. But, can augmented reality, along with robotics and virtual reality work together? Yes! Together, augmented reality, robotics, and virtual reality can be the ‘Three Amigos.’ VR and AR can offer an immersive medium to operate robots. With the help of low-latency networks, people can utilize robots remotely using intuitive AR and VR controls. Augmented reality, robotics, and virtual reality can be used together in various industry sectors such as manufacturing, healthcare, and private space research.
Machine learning is one of the hottest topics in technology today. Parallel to the success of learning algorithms, the development of quantum computing hardware has accelerated over the last few years. In fact, we are at the threshold of achieving a quantum advantage, that is, a speed or other performance boost over classical algorithms, in certain specific application areas – machine learning being one of them. This sounds exciting, but don’t ditch your GPU cluster just yet; quantum computers and your parallel processing units solve different classes of problems.
Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields.
Think of the old paradigm where a software sales person or team would shuffle into your conference room and deliver a packaged presentation, followed by a packaged demo, in hopes of finding a hot button that appealed to you and somehow satisfied your need. That won't work today and a wise software company knows that. If you want to succeed in the market today, you have to get ahead of things. You need to understand what your prospective customers want, and need, and take into consideration the industry in which they work.
While the same core technologies that dominated discussions will continue to be foundational to our collective digital transformation journey, 2020 will be defined by a fresh new class of technologies ready to graduate to the sidelines to center stage. Among them: 5G, AI, advanced data analytics, but also some that may surprise you. Without further ado, here are the 10 among them that will be the most significant in 2020, and will both dominate digital transformation discussions and inform the trajectory of successful digital transformation programs.