Distributed microservices at scale can create a tremendous volume of network traffic between individual containers; a leading concern is the potential increase in east-west traffic in the data center and even between container-based applications within a single server. Key challenges for networking containers include performance, automated provisioning of appropriate network resources, visibility and network management. Network security is another issue. Containers solve some security concerns, like isolation, but may create other unknown vulnerabilities. Some current security technologies will easily support the migration to containers, while others may not. Networking can be built into container software or provided by third-party network software, such as Cumulus Networks, Pluribus Networks, 128 Technology and Big Switch Networks.
Supervised learning and unsupervised learning are the most popular approaches to machine learning. Both require feeding the machine a massive number of data records to correlate and learn from. Such collected data records are commonly known as a feature vectors. In the case of an individual house, a feature vector might consist of features such as overall house size, number of rooms, and the age of the house. In supervised learning, a machine learning algorithm is trained to correctly respond to questions related to feature vectors. To train an algorithm, the machine is fed a set of feature vectors and an associated label. Labels are typically provided by a human annotator, and represent the right "answer" to a given question. The learning algorithm analyzes feature vectors and their correct labels to find internal structures and relationships between them. Thus, the machine learns to correctly respond to queries.
SAFe scales by combining the power of agile with lean product development, and systems thinking. It creates alignment between strategy and execution from the portfolio to agile teams and vice versa. The basic building block for SAFe’s scalability are Agile Release Trains (ARTs). An ART is essentially an agile program, which contains between five to twelve agile teams that are all collaborating together, as one team, via a common mission, vision, and program backlog. If you are building a solution that requires the contributions of hundreds—or even thousands—of people, you simply launch more trains and coordinate them following the same patterns and similar roles used to coordinate multiple Agile teams. Face-to-face planning and integrated system demos helps assure collaboration, alignment, and rapid adaptation.
“In virtualisation management, where you might be managing tens of thousands of virtual machines, the level of automation is already an order of magnitude higher, and it’s higher again with containerisation,” Hubbard said. “To IT administrators, that’s helpful. So when you ask, ‘Are you threatened by automation?’, they will say no. But the automation is replacing a full time job.” New jobs, however, are emerging, according to companies already implementing AI. In a Capgemini survey of almost 1,000 organisations which are implementing AI, either as a pilot or at scale, 83% of respondents said AI had generated new roles in their organisations. Among those that had deployed AI at scale, 63% said that no job had been axed. Nevertheless, AI technologies are being rolled out in Australia with the capacity to significantly disrupt traditional roles.
MongoDB uses the BSON format under the hood to represent the JSON documents at the heart of the data store. BSON or “Binary JSON” is a lightweight and efficient binary-encoded data serialization format that supports fast data traversal and searches. BSON also allows MongoDB to support data types—namely int, long, date, floating point, and decimal128—not represented in JSON. In MongoDB documents are part of collections, in much the same way as a row is part of a table in a relational database. A document is essentially a collection of field and value pairs, which can also be nested. Note that a value in MongoDB can be a document, an array of documents, an array of BSON, or just a BSON type. Let’s look at how we can work with MongoDB using C#.
Digital forensics is the extraction, analysis, and documentation of data from physical media. Why it matters: Digital life is not anonymous. As we use the web, we also scatter fragments of data in our wake. If collected, personal data fragments can present an accurate profile of our behavior and personality. Often this data trail is accompanied by legal implications. Digital forensic experts know how to assemble the picture. Who it affects: Because digital forensics experts are typically used in a legal setting, government organizations, SMBs, and enterprise companies may want to consider preemptively working with an expert to better understand potential vulnerabilities. When it's happening: Digital forensics has been a thriving industry since the mid-1970s.
Interacting with a machine via natural language is one of the requirements for general artificial intelligence. This field of AI refers to dialogue systems, spoken dialogue systems, or chatbots. The machine needs to provide you with an informative answer, maintain the context of the dialogue, and be indistinguishable from the human (ideally). In practice, the last requirement is not yet reachable. But luckily, humans are ready to talk with robots if they are helpful — sometimes, they can even be funny and interesting interlocutors. There are two major types of dialogue systems: goal-oriented and general conversation. The former help people to solve everyday problems using natural language, while the latter attempt to talk with people on a wide range of topics.
Video editing software ranges from free versions that are pretty bare-bones to feature-packed prosumer versions. Indeed, they vary as much as the reasons why people take up video editing—whether to make home videos, to become YouTube stars, to create VR experiences, and more. Most video editing software for consumers and mainstream users is best used for one or another of these specific functions, but there are a few generalists out there, too. For this roundup we’ll first be looking at the middle ground: Paid consumer video editing programs that cost $80 or less. Whatever your purpose, you should be able to find consumer software for less than $100 that can meet your needs. We’ll soon be updating this roundup with our top picks among free versions and prosumer versions, so stay tuned for more reviews.
"We're experiencing a period that's very exciting, because there is a lot of innovation going on and different parties racing to deploy new applications, devices, and techniques," Domingo Guerra, co-founder and president of Appthority, said in a panel discussion. However, not enough attention is being paid to the potential risks. "We've seen it before where we deploy smart traffic grids or street lights and never think about how to secure it or patch it until it's too late and too costly to address," Guerra said. "The main risk is not enough caution and foresight into how to address this new innovation securely." Many IoT device manufacturers do not include security in the design phase, said David Schwartzberg, senior security engineer at MobileIron. These manufacturers analyze their project from a cost perspective and time to delivery, and security often falls by the wayside.
Quote for the day:
"Before you attempt to set things right, make sure you see things right." -- John Maxwell