You can now use the Cloud Logging datasource plugin to view your logs in Grafana.

Cloud storage has become an essential component for businesses of all sizes. It allows you to store and access your data on a remote server, providing flexibility, scalability, and cost-effectiveness. However, with multiple cloud storage providers available, it can be challenging to choose the right one for your business needs. In this article, we will compare the three most popular cloud storage providers, GCP, Azure, and AWS, to help you make an informed decision.

Many of the most disruptive tech developments, such as artificial intelligence (AI), virtual reality (VR), and remote and hybrid working platforms, have been fueled by the continuous broad use of cloud computing. According to a 2022 report by Statista, a large majority of businesses now use multiple public cloud providers, with 90% of large enterprises and 60% of small businesses currently implementing a multi-cloud strategy.

With containerization, you can write an application once and run it anywhere. Along with portability, containers are also much more resource-efficient than traditional virtual machines (VMs). However, managing containers at scale can be challenging. Besides, many teams find container orchestration platforms like Kubernetes overkill and too complex.

Today I’m going to speak about another important Best practice, Tips and Tricks that you need to consider while designing your business processes (Logic Apps): Moving or Organizing shapes inside Visual Studio Logic App designer.

As more and more applications move to the cloud, it becomes increasingly important to design them for high availability, scalability, and reliability. One of the key components of building such applications is message queuing, which allows you to send and receive messages between different parts of your application. Azure Queues is a service provided by Microsoft Azure that enables developers to build highly scalable and distributed applications that can handle large volumes of messages.

The following blog was written together with Petar Torre, Solutions Architect at Intel. This blog describes how Cloudify automates the deployment and monitoring of Machine Learning systems, by orchestrating an Intel-optimized TensorFlow workload running inference with a pre-trained ResNet-50 model from the Intel Model Zoo. In a nutshell, a container running a Jupyter Notebook with the Intel optimized TensorFlow model is scheduled as a Kubernetes pod on K3S on AWS EC2.

Nowadays, when most people think about the term “machine learning,” they think of advanced, refined applications such as Chat-GPT, the chatbot-based deep learning text generator, or AlphaGo, the computer program that’s currently the “world's best player” of the board game Go.