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Deep learning is an important part of the business of Google, Amazon, Microsoft, and Facebook, as well as countless smaller companies. It has been responsible for many of the recent advances in areas such as automatic language translation, image classification, and conversational interfaces.
We haven’t gotten to the point where there is a single dominant deep learning framework. TensorFlow (Google) is very good, but has been hard to learn and use. Also TensorFlow’s dataflow graphs have been difficult to debug, which is why the TensorFlow project has been working on eager execution and the TensorFlow debugger. TensorFlow used to lack a decent high-level API for creating models; now it has three of them, including a bespoke version of Keras.
ASP.Net Web API is a lightweight framework that can be used for building RESTful HTTP services. When working with controller methods in Web API, you will often need to pass parameters to those methods. A “parameter” here simply refers to the argument to a method, while “parameter binding” refers to the process of setting values to the parameters of the Web API methods.
Note that there are two ways in which Web API can bind parameters: model binding and formatters. Model binding is used to read from the query string, while formatters are used to read from the request body. You can also use type converters to enable Web API to treat a class as a simple type and then bind the parameter from the URI. To do this, you would need to create a custom TypeConverter. You can also create a custom model binder by implementing the IModelBinder interface in your class and then implementing the BindModel method. For more on type converters and model binders, take a look at this Microsoft documentation.
According to a recent report from IDC, “worldwide revenues for big data and business analytics will grow from nearly $122 billion in 2015 to more than $187 billion in 2019, an increase of more than 50 percent over the five-year forecast period.”
Anyone in enterprise IT already knows that big data is a big deal. If you can manage and analyze massive amounts of data—I’m talking petabytes—you’ll have access to all sorts of information that will help you run your business better.[ The essentials from InfoWorld: What is big data analytics? Everything you need to know • What is data mining? How analytics uncovers insights. | Go deep into analytics and big data with the InfoWorld Big Data and Analytics Report newsletter. ]
Right? Sadly, for most enterprises, no.
One key devops best practice is instrumenting a continuous integration/continuousdelivery (CI/CD) pipeline that automates the process of building software, packaging applications, deploying them to target environments, and instrumenting service calls to enable the application. This automation requires scripting individual procedures and orchestrating the steps from code checkin to running application. Once matured, devops teams use the automation to drive process change and strive to do smaller, more frequent deployments that deliver new functionality to users and improve quality.
Sebastian Stadil is the CEO and founder of Scalr.
Enterprises are moving to multicloud in droves. Why? The key drivers most often cited by cloud adopters are speed, agility, platform flexibility, and reduced costs—or at least more predictable costs. It’s ironic then that more than half of these companies say that runaway cloud costs are their biggest postmigration pain point.
Sebastian Stadil is the CEO and founder of Scalr.
Enterprises are moving to multi-cloud in droves. Why? The key drivers most often cited by cloud adopters are speed, agility, platform flexibility, and reduced costs—or at least more predictable costs. It’s ironic then that more than half of these companies say that runaway cloud costs are their biggest post-migration pain point.
The power of Docker images is that they’re lightweight and portable—they can be moved freely between systems. You can easily create a set of standard images, store them in a repository on your network, and share them throughout your organization. Or you could turn to Docker Inc., which has created various mechanisms for sharing Docker container images in public and private.
The most prominent among these is Docker Hub, the company’s public exchange for container images. Many open source projects provide official versions of their Docker images there, making it a convenient starting point for creating new containers by building on existing ones, or just obtaining stock versions of containers to spin up a project quickly. And you get one private Docker Hub repository of your own for free.
I hear it every day now: “We’re moving beyond cloud computing to edge computing.” Pretty hypey, and not at all logical.
Edge computing is a handy trick. It’s the ability to place processing and data retention at a system that’s closer to the target system it’s collecting data for as well as to provide autonomous processing.[ What is cloud computing? Everything you need to know now. | Also: InfoWorld’s David Linthicum explains what exactly is edge computing. ]
The architectural advantages are plenty, including not having to transmit all the data to the back-end systems—typical in the cloud—for processing. This reduces latency and can provide better security and reliability as well.
The Xamarin acquisition was one of Microsoft’s smartest deals. It quickly gave it access to tools that let developers use familiar tools and technologies to build cross-platform applications. Now built into every version of Visual Studio, and providing the basis for its MacOS Visual Studio release, Xamarin has become a key element of Microsoft’s development tools.
Until recently—even with Xamarin—building cross-platform applications wasn’t easy. For all that the core development tools handle working with iOS and Android from .Net, using it to build apps meant having significant amounts of device-specific code to handle both native UX and deep platform integration. Although you could keep your core code across device-specific projects, building and testing the full application required domain knowledge and specialized skills. The result was code that, although a little cheaper than using native tools for each platform, really wasn’t as cheap to build as it could have been.