Using multiple algorithms and tuning the algorithms to find the optimum value for each parameter also improves the accuracy of the model. However, it is not necessary that higher accuracy models always give the accurate results, as sometimes, the improvement in model’s accuracy can be due to over-fitting too.
Before embarking on building your own extension, I would endorse looking through the Marketplace for a solution, when I last looked there were over 1250 extensions available to add to your system.
I had to come up with a way to magically inject a script on every response, regardless of what technology was used to create the application or web page… believe it or not, this was not only possible but it is quick and simple!
Machine learning is the scientific study of algorithms and statistical models that result in devices automatically learning and improving from experiences without being explicitly programmed. With so much success integrating machine learning into our everyday lives, the obvious next step is to integrate machine learning into even more systems.
So you have a fairly mature DevOps model in-place but you are missing the ability to integrate Azure Support issues into your processes. Well, that is no longer the case as we have released to the public our Azure Support Ticket REST API.
I was recently working with a customer who has an application running in a Windows container and that application outputs log files into different folders inside of the container. Their log extraction process was to manually remote desktop into the node, then go into the container to get the logs out.
Azure DevOps provides features that enable your organization and teams adopt modern software engineering practices, such as InnerSource using features like forks and pull requests. Thus, enabling and encouraging code sharing and reuse thereby reducing engineering costs, facilitating faster time to market, and additional feature enablement.
In this post, I will go through the broad strokes of setting up Office 365 Education. The starting point is the Office 365 A1 trial sign up page.
Telemetry Processor is a plug-ins that can be ingested in your application to customize how telemetry can be processed before it’s sent to the Application Insights service such as filtering out telemetry, replace or discard a telemetry item, etc.
With microservice architecture being as popular as it is today, being able to Mock your services makes it easy to get all your developers coding right away. We no longer have to wait for backend services to be completely developed or spend time coding throw away responses to provide our calling applications.
