Azure Cognitive Services in Containers

Do we really need data scientists to create a solution using machine learning? 

Well, previously, yes. It’s a must. 

But now thanks to Azure cognitive services, those can be done by a developer. 

If a developer wants to take the advantage of AI and features cognitive service provides which is vision, speech, search, text processing, language understanding this will be the best way.

Anyone who can write a simple program, means who writes basic code can use this feature and create AI models using AML. But the problem is scaling. Most of the time model developers facing this problem and now there is a solution for that too. Container Support for Cognitive Service. It is much easier to use and just build and deploy.

This allows developers to build big AI systems that run at scale, reliably, and consistently in a way that supports better data governance.

Customer Lockbox for Azure

Azure is very user-friendly and has plenty of documentation that users can read and understand very easily. As an administrator, you might find ample knowledge to handle the situations that can disturb the live systems at times. There are clear guidelines for troubleshooting most of the issues that come in. You have proper UI, if not logs that can be accessed via scripts. 

But after all, cloud is someone else's computer. In Azure's case, it is Microsoft's. So there can be situations where all those lengthy detailed documentations that are provided by Microsoft can also not be enough to handle some situations. But these instances are very rare. 

Azure Monitor for Containers

Microsoft Azure team has released a useful tool for Azure Kubernetes Service (AKS) called Azure Monitor: A container health monitoring tool. It’s right now in public preview and the developers are welcome to work on it. This enables to developers to track the health and performance of your Azure Kubernetes Service (AKS) cluster. As it is said, Microsoft will release new features and updates to on-boarding and the portal experience later.

Nodes have a lifecycle of fail and reboot. Therefore it is important that things are kept under watch. Not that they fails almost, but there can be instances. Azure Monitor assures such instances are monitored by collecting the data from processors and memory allocations. Not just them, the logs are also monitored and kept in places. There is a Log Analytics agent for Linux available for collecting purposes. Those logs are stored in the Log Analytics workspace.