High-end cameras, especially on smartphones, are an easy and qualitative source of data acquisition, directly in the field. When it comes to industrial maintenance, any technician has a tool in his pocket to upload an image or video and consult an AI to find the solution to the issue. In agriculture, a farmer can take a picture of a crop and immediately have information about a potential disease. Drones have also become an important part of computer vision, especially for agricultural or large industrial installations (power lines, recycling plants, pipelines, etc.). The ability for drones to fly over large areas means that cameras can collect images that would have been far too cost-prohibitive even a few years ago. ... Edge infrastructure addresses this challenge by analyzing the footage locally and uploading only a fraction of its data for further analysis. With video, data privacy and security are extremely sensitive, especially compared to devices such as agricultural soil sensors. Storing the files locally on edge devices can reduce the risk of their hacking, but above all clarify the responsibilities in the case of data robbery (site manager, client).
Some people. Not to focus on unit tests, but focus more on integration and system tests. I belong to the team unit testing. So in my book, I do recommend people to try to focus on unit testing as much as possible, the reason being I believe that if you design your code, well, the core of your system, the important parts of your system will be just a. For loops and F’s and data structures being manipulated. And those can be easily tested with unit testing and by easily, I mean, it’s super easy and fast to write a test, they run super fast. You can quickly explore different corner cases. You know, it’s super easy to just instantiate a class, put some values in column methods. That is why I prefer unit [00:16:00] testing. But that requires though that you develop your system with this, you know, unit testing, the stability in mind, and this is not always. Why do some people prefer integration testing and they have a point there because, you know, in lots of types of systems, we do a lot of the bugs only happen when you put components together. ... And if you’re really mocking out components, you know, when testing one component, you kind of mock the rest, maybe you’re going to meet.
To maximize the value of their data as it’s created — instead of waiting hours, days, or even longer to analyze it once it’s at rest—Overstock needed a streaming and messaging platform, which would enable them employ real-time decision-making to deliver personalized experiences and recommend products likely to be well-received by customers at the perfect time (really fast, in other words). Data messaging and streaming is a key part of an event-driven architecture, which is a software architecture or programming approach built around the capture, communication, processing, and persistence of events—mouse clicks, sensor outputs, and the like. Processing streams of data involves taking actions on a series of data that originates from a system that continuously creates “events.” The ability to query this non-stop stream and find anomalies, recognize that something important has happened, and act on it quickly and in a meaningful way, is what streaming technology enables. This is in contrast to batch processing, where an application would store a data after intaking it, process it, and then store the processed result or forward it to another application or tool.
Is MLOps for real? It’s happening. With services such as auto ML, it’s getting easier to scale-out machine learning models. OctoML is based upon Apache TVM, an end-to-end machine learning compiler framework for CPUs, GPUs and accelerators. Apache TVM is a project originating from the University of Washington. TVM stands for tensor virtual machine. It provides a common layer across targets that exposes a clean interface to the upper layers of the stack, and machine learning frameworks, such as TensorFlow and PyTorch. ... What Apache TVM does is create a set of common primitives across all sorts of different hardware from embedded CPUs to service CPUs, small GPUs, large GPUs, accelerators, and so on. And then it uses machine learning internally to produce efficient machine learning code. Okay, so any section here uses machine learning for machine learning, code optimization. The reason that’s important is because by and large today, the work done to get a model ready for deployment is manual.
On the mobile side, users can deploy profiles and policies; configure devices for Wi-Fi, VPNs, email accounts and so on; apply restrictions on application installs, camera usage and the browser; and manage security with passcodes and remote lock/wipe functionality. As such, the platform offers far-reaching access into the guts of an organization’s IT footprint, making for an information-disclosure nightmare in the case of an exploit, potentially. As well, the ability to install a .ZIP file paves the way for the installation of malware on all of the endpoints managed by the Desktop Central instance. In the case of the MSP version – which, as its name suggests, allows managed service providers (MSPs) to offer endpoint management to their own customers – the bug could be used in a supply-chain attack. Cybercriminals can simply compromise one MSP’s Desktop Central MSP edition and potentially gain access to the customers whose footprints are being managed using it, depending on security measures the provider has put in place.
With Continuous Integration, we are able to find bugs as soon as they are introduced. This leads to shipping releases faster and with better quality. In addition, Continuous Integration eliminates manual handoffs and ensures that releases are frequent. This way, developers can focus on writing code for new features rather than fixing bugs manually all day long. It saves time, effort, and increases developer morale towards the work. In most organizations, the friction in software delivery is because most of the tasks are manual and error-prone. For example, someone provisions/updates the environment as needed, a team deploys a specific version of the software, and another team keeps track of what’s running where. This creates a dependency hell and lack of visibility. Ultimately, these inefficiencies lead to slower software releases and lost revenue and opportunities. The best approach to achieve velocity is to automate all steps in the deployment pipeline. This leads to more frequent and rapid release cycles that are predictable and error-free and will lead to happy and productive engineering teams.
One big difference is the maturity of regulation. Bitcoin was born out of the financial crisis and the subprime mortgage meltdown of 2008/2009. It tapped the visceral reaction to a system that favors large financial institutions and hurts the average person. You remember the movie “The Big Short”? Christian Bale’s character couldn’t understand why, when real estate markets were cratering around him, that his “insurance policy” wasn’t skyrocketing in value. The reason was the big banks, likely with government knowledge, were unwinding their positions to reduce the damage. Once they limited their downside exposure, the market crashed in dramatic fashion. Watch this clip of Stephen Colbert interviewing Michael Lewis, author of “The Big Short.” ... Bitcoin specifically (and cryptocurrency generally) is the confluence of cryptography, software engineering and game theory – all well-understood and applied disciplines. The blockchain and cryptocurrency can cut out the so-called trusted third party and enable direct, highly secure transactions between two parties.
Perhaps the main issue with edge computing is that it is really a bunch of diverse applications that have been lumped together into one category, simply because they operate outside the bounds of the traditional data centre. However you choose to define it, it looks like it isn't going away. In Red Hat's 2022 Global Tech Outlook report, edge computing was listed among the emerging technology workloads that organisations are most likely to consider over the coming year. In fact, if you consider edge and IoT to overlap somewhat, the two combined were the leading category, with 61 per cent of respondents saying they were considering one or both. Red Hat itself defines edge computing as a distributed computing model in which data is captured, stored, processed and analysed at or near the physical location where it is created. The firm says that it views it as "an opportunity to extend the open hybrid cloud all the way to the data sources and end users." ... Because workloads can reside across a continuum of core, edge, and endpoint locations, edge computing requires a significant amount of coordination among technology and service providers, it says.
Developers are writing less logic and spending more time gluing things together. Today the average production system has interactions with multiple databases, APIs, and other microservices and endpoints. Any time your software has to talk to a different piece of software, you can no longer make simple assumptions about how your system is going to behave. Every database, message queue, cache, and framework has its own particular states, rules, and constraints that determine its behavior. Developers need a way to test these behaviors in advance of deployment, and this class of testing is called integration testing. ... There will always be impassioned debates about how to best balance speed versus software quality. One reason for the great popularity of the Java compiler and similar technologies has been their ability to help developers find failure closer to the point of development so they can fix them quickly. There will always be diabolical bugs that evade your testing, but with the increasing ease of software unit testing and integration testing today, it’s getting harder to credibly argue against investing more cycles into testing your code and its integration surface before pushing to production.
Quote for the day:
"Focusing on others will give you more influence and power than focusing on yourself." -- Kevin Eikenberry