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
A Data Catalog is a crucial component within the realm of Data Governance that serves as a centralized inventory or repository containing detailed metadata and information about an organization’s data assets. It acts as a comprehensive reference guide or index, enabling users to easily discover, understand, and access available data resources within an organization. In
Data Governance refers to the framework and set of processes that ensure high data quality, accessibility, security, and usability within an organization. It involves defining policies, procedures, and responsibilities to manage data assets effectively throughout their lifecycle. The primary goal of Data Governance is to establish controls and guidelines for managing data as a valuable