In the rapidly evolving landscape of cloud-native infrastructure, developers and cloud operators face a myriad of challenges in ensuring effective observability. The dynamic nature of these environments, coupled with the prevalence of technologies like Kubernetes and the diversity of deployment models (public, private, and hybrid cloud), presents a complex observability landscape. Understanding and addressing these
What does it mean to be “cloud native”? Cloud-native infrastructure refers to the design, deployment, and management of IT resources that leverage cloud computing principles. It involves building and running applications that take full advantage of the dynamic, scalable, and distributed nature of cloud environments. Characteristics include microservices architecture, containerization (e.g., Docker), and orchestration tools
In an earlier blog post, Kubernetes Overview, we introduced Kubernetes. We introduced key capabilities of Kubernetes i.e. Portability, Extensibility, Declarative Configuration and Automation. Let’s take a closer look at these capabilities to acquire deeper insights into the technology. Portability The portability of Kubernetes refers to its ability to enable consistent and seamless deployment and management
In today’s digital world, many services we rely on, like online shopping, social media, and streaming, run on complex software systems. When something goes wrong with these systems, it can affect thousands or even millions of users. Observability helps engineers understand how these systems are performing and quickly find and fix issues. This blog post
Kubernetes is an open-source platform for automated orchestration and management of Containerized applications. Before dwelling into the specifics, let’s briefly explore its origins. Kubernetes is a Greek word, meaning “helmsman” or a “pilot”. Kubernetes was originally developed at Google to run containerized workloads, then released as open source in 2014 and maintained by the Cloud
Natural Language Processing (NLP) gives the machines the ability to read, understand and derive meaning from human languages. Nearly 90% of data generated today from various channels is unstructured such as email, social media, news feeds & blogs, text and OTT messages, audio, video and more. Some of the real-world applications of NLP include sentiment
In the world of artificial intelligence (AI) and data science, we often hear about supervised and unsupervised learning. However, there is a powerful and increasingly popular middle ground known as Semi-Supervised Machine Learning. This approach combines the best of both worlds, using a mix of labeled and unlabeled data to train models. This article will
Unsupervised Machine Learning is a powerful technique in the field of artificial intelligence (AI) and data science. Unlike supervised learning, which uses labeled data to train models, unsupervised learning works with data that has no labels. The goal of unsupervised learning is to find hidden patterns and structures in the data. This article will define
Supervised Machine Learning is a foundational technique in artificial intelligence (AI) and data science. It involves training a machine learning model on a labeled dataset, where each input data point is paired with the correct output label. The goal of supervised learning is to learn a mapping from inputs to outputs, allowing the model to
Introduction In the fascinating world of artificial intelligence (AI), two fundamental concepts are crucial: machine learning algorithms and machine learning models. Although these terms are often used interchangeably, they serve distinct roles in the machine learning process. Machine learning algorithms are the procedures or methods used to learn from data, while machine learning models are