Twenty-five years ago, some fellow students and I were sitting in a computer lab at the University of Waterloo trying to compile a new open-source UNIX operating system called Linux on a PC. Back then, installing a Linux system was about as difficult as nailing Jell-O to a tree, but we managed to get a system installed after only four days of work. Linux has come a long way since then. Today, Linux is the most diverse and aggressively developed operating system in the world, primarily due to its open-source nature. And if you work in an IT field, you’ve probably been exposed to more Linux in the last few years than before. In fact, the Gartner research company identified Linux as the fastest-growing operating system segment in the computing industry in 2017. So, what does this mean for you as an IT professional? It means that you’ll likely be working with far more Linux systems and technologies in coming years, regardless of whether you currently work with them or not.
The SD-WAN is typically made of diverse networks and technologies that many times are outside the control of IT. Add to that the increased use of multi-cloud services and other advances, and the traditional complexity of the WAN has been increased, Cisco stated. Cisco cited a recent IDC study that found almost three out of 10 organizations considered network outages to be a top WAN concern, with the same number stating they need better visibility and analytics to manage application and WAN performance. IDC also estimates that worldwide SD-WAN infrastructure and services revenues will hit $8.05 billion by 2021. In order to address some of these challenges, Cisco rolled out SD-WAN vAnalytics, a cloud-based SaaS application that will collect data from the SD-WAN and let customers spot and fix communications problems quicker, gauge application performance, oversee bandwidth planning, and predict how policy changes might impact the network.
Cloud-based big-data silo convergence is speeding enterprise time-to-value. Users are beginning to step up the pace of consolidation of their siloed big data assets into public clouds. The growing dominance of public cloud providers is collapsing the cross-business silos that have heretofore afflicted enterprises’ private big data architectures. Just as important, big data solutions, both cloud-based and on-premises, are converging into integrated offerings designed to reduce complexity and accelerate time to value. More solution providers are providing standardized APIs for simplifying access, accelerating development, and enabling more comprehensive administration throughout their big data solution stacks. Innovative big data startups are bringing increasingly sophisticated AI-infused applications to market. Innovative application providers are starting to disrupt the big data competitive landscape with AI-based solutions.
For all the obvious reasons. (1) People don't really care how much you know until they know how much you care. Showing up shows them that you actually do care. (2) Startups are notoriously scattered and in a hurry. Focus and attention to detail are scarce commodities and the customers want to know that you personally are connected, paying attention, and directly engaged with their business, their concerns and their problems. And finally, (3) they want to hear it from the horse's mouth. Not second hand. They want commitments and assurances from you (since they know that the sales guys will tell them anything and promise them the world) that you will stand up for and stand behind your product or service and make good on whatever they've been promised. The buck always stops with you. None of this is very tough. You just have to say what you're going to do and do what you said you would and everything will be hunky-dory.
Why would you need a vCISO when you could simply hire a real one on a permanent contract? The answer is varied and not necessarily the same for everyone. For starters, well-rated, full-time CISOs can be hard to come by, often stay in their job for two years or less, and critically, especially for smaller businesses, can command six-figure salaries. In contrast, vCISOs are estimated to cost between 30 percent and 40 percent of a full-time CISO and are available on-demand. The benefits go well beyond cost. Virtual CISOs usually require no training, can hit the ground running, and don’t feel obliged to play nice with office politics. In this model, it’s purely about results, and vCISOs worth their salt will provide reasonable KPIs and reporting. While different vCISOs offer different skillsets, many should be able to cover myriad tasks, from the tactical to strategic. They could help pull together security policies, guidelines and standards. That could entail anything from coming to grips with HIPAA or PCI compliance, to staying on top of vendor risk assessment.
Bersin reminds us that, even though the top, hot job of the moment may be technical, there are are plenty of non-technical jobs that are growing in demand, too. “Soft skills are just as in demand as hard skills. There will be an increased need for social, integrative, and hybrid skills. Empathy, communication, speaking, judgement… these renaissance skills are the jobs of the future,” said Josh. “Even the job of data scientist now requires persuasion, interpretation, not just looking at data.” Although many worry that technology will render some workers obsolete, this appears to be far from the case. Many of these workers can easily transition into new roles that leverage their skills, and these new roles are good for the workers, too. In fact, 96% of all transitions have “good-fit” options and 65% of transitions will increase wages.
In the case of supervised learning, you train a model to make predictions by passing it examples with known inputs and outputs. Once the model has seen enough examples, it can predict a probable output from similar inputs. ... The results of the prediction can’t be better than the quality of the data used for training. A data scientist will often withhold some of the data from the training and use it to test the accuracy of the predictions. With unsupervised learning, you want an algorithm to find patterns in the data and you don’t have examples to give it. In the case of clustering, the algorithm would categorize the data into groups. For example, if you are running a marketing campaign, a clustering algorithm could find groups of customers that need different marketing messages and discover specialized groups you may not have known about. In the case of association, you want the algorithm to find rules that describe the data.
Companies that have been slower to invest in technology solutions have either prioritized changing their business model or have felt the negative, if not fatal, repercussions of not doing so. Regardless of industry, staying ahead of the technological curve in today’s software-centric world is a must for business success. However, it can be difficult for even the most experienced IT leaders to wade through the long list of technology buzzwords and solutions that promise to be the “next best things.” So how can businesses cut through the noise to determine what will actually bring business value? They can start by determining the technologies the experts are actually pursuing. To find out what these tech trends are, O’Reilly analyzed search data from more than two million users on its online learning platform, most of which are trained software and technology leaders. By taking into consideration what these professionals are focusing on, other professionals can begin to determine what their companies should be investing time and money in.
Balshai said, "We have moving, navigation, a manipulation of an arm, computer vision. Everything combined in one platform. Basically to be able to master all this knowledge and be able to find the algorithm for making it work is really complex. With ROS, it gives us a lot of opportunity to combine algorithms from one point to another. For example, if something was developed in a Carnegie Mellon University in the United States and we want to use this particular system, image work, or cognition of an object that was developed in Carnegie Mellon, we can extract this information and extract these ideas and implement it in our robot real easily. "By that, we don't need to have a really huge company to be able to do a lot of different tasks with one robot. This is basically the idea and the advantage of using ROS and open source architects for how we use robotics. By doing something that is generic for everybody, you can use it all over the globe. Of course, there is stuff that we extract to others. ..."
Data mining sits at the intersection of statistics (analysis of numerical data) and artificial intelligence / machine learning (Software and systems that perceive and learn like humans based on algorithms) and databases. Translating these into technical skills leads to requiring competency in Python, R, and SQL among others. In my opinion, a successful data miner should also have a business context/knowledge and other so called soft skills (team, business acumen, communication etc.) in addition to the above mentioned technical skills. Why? Remember that data mining is a tool with the sole purpose of achieving a business objective (increase revenues / reduce costs) by accelerating the predictive capabilities. A pure technical skill will not accomplish that objective without some business context. The following article from KDNuggets proves my point that data mining job advertisements mentioned the following terms very frequently: team skills, business acumen, analytics among others.
Quote for the day:
"Vulnerability is the birthplace of innovation, creativity and change." -- Brené Brown