ML model drift is the phenomenon whereby a machine learning model's performance on a given task deteriorates over time. This can happen for a variety of reasons, including changes in the underlying data distribution, changes in the model's environment, or even changes in the way the model is used..

MLOps stands for Machine Learning Operations. It is a central part of machine learning engineering, and aims to simplify and automate the process of building machine learning models, deploying them to production, monitoring, and maintaining them over their lifecycle.
MLOps is a collaborative function jointly performed by data scientists, data analysts, DevOps engineers, machine learning engineers, and software developers..

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Application security (AppSec) is an integral part of the development lifecycle, ensuring applications are built with security against various threats, including insider threats and malicious intrusion. Internal threats can occur due to human error and malicious acts like phishing schemes, and external threats can involve malware and injection attacks.

Endpoint protection tools and practices enable you to protect a network against endpoints and entry points, including desktops, mobile devices, and laptops. It aims to ensure that if any of these endpoints are compromised by malicious actors or campaigns, the rest of the network remains unaffected.

Azure cost optimization lets you reduce cloud spending by matching the infrastructure you use in the Azure cloud to your actual requirements. The better the match, the lower your cloud costs. To take a simple example, if you are running virtual machines (VMs) on Azure and they are not utilized at all, this represents a mismatch between your cloud infrastructure and business needs. By shutting down the VMs, you can conserve costs.

Most people often mix the terms Data Science, AI, Machine Learning, and Data Mining or use some of them interchangeably. But all of these terms have their separate meaning and stream. Data has always been a central element of any business to make precise decisions from granular data. Even though all of these terminologies are somehow interrelated to data, each has its characteristics. This article will give you a clear understanding of all these four terminologies and their differentiation..