The single largest problem with ML in network analysis is that each network is unique and, more importantly, is constantly changing.
We live in a world where machines, systems, processes, people, and data are all interconnected in an intelligent, efficient and secure fashion. Immense connectivity has led to cognitive transformation that helps businesses to streamline processes and enhance customer experience by gathering more real-time data, making smarter decisions, offering better business insight, and improved flexibility.
Machine learning (ML) is a hot buzzword. Every business and network analytical tool is looking for ways to differentiate its offering, usually by claiming that more intelligence has been embedded in it.
The rules of business are being redefined daily. In fact, change is happening more quickly than companies can adapt. While many organizations have their digital transformations well underway, even those with vast resources are struggling to keep up with the rapid pace of new developments in technology, communications and consumer interests. This is especially true for organizations looking to grow revenue and profitability by adopting digital innovation processes to accelerate product development and to better manage product lifecycles.
As a Systems Engineer who recently underwent a home renovation, I was able to draw parallels between the product development work I do, and that arduous task.
The number of technology-related job opportunities is expected to increase 12 percent by 2024 according to a report by Modis. This might be a great stat for people entering the industry, but it also means that competition for this talent is going to commensurately increase. Anyone who’s been in our industry over the last decade knows this isn’t anything new; just five short years ago we were already struggling to get people to apply.
Once your organization has embraced and implemented a DevOps approach to software development, the work shouldn’t end there. Your team may have a streamlined DevOps strategy that fosters a culture of continuous delivery, but they still need to avoid falling into the trap of a drifting or coasting mentality.
On the face of it, nobody should be using floppy disks in the 21st Century. Surprisingly, however, parts of the aviation industry still rely on these disks to manage airplane software.
While your calendar is flurried with holiday dates, you should already be aware of one deadline – Dec. 31, 2017. It’s the date by which anyone holding U.S. government data must comply with the security directives surrounding controlled unclassified information (CUI), known as National Institute of Standards (NIST) Special Publication (SP) 800-171, Revision 1.
Product development programs all start with great optimism regarding their potential to provide an excellent product to customers, along with excellent profits to the company. But research shows that a majority of product developments end in failure. The reasons for each failure are unique, but the overarching reason often can be described as the lack of systems engineering.