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..
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..
Artificial intelligence is software that performs tasks without human intervention or minimal intervention. To achieve this, developers and data scientists implement machine learning (ML) processes. These are essentially complex algorithms that teach machines various learning techniques.
A notable sub-field of ML is deep learning (DL), which has been said to significantly aid the development of various autonomous capabilities of AI..
With AI and ML advanced algorithms, banking sectors use NLP to automate the various processes. The procedure includes document processing, analyzing, and activities related to customers for hassle-free services. The banking industry is one of the critical sectors for more than decades, and lots of transactions take place at every moment. And it is always a huge challenge to have everything on the record without a series of automation processes..
Many businesses struggle to make the right decisions, even with having experts in different fields, despite business goals and objectives. With the massive database, it is often difficult to classify and make accurate decisions with predictive analytics. Therefore, decision trees are one of the most sought-after algorithms for effectively making critical decisions. It does not mean that the algorithms are complicated, but they are understandable to others..
Language is very important for communication, it works like a tool. According to a google search, there are 7117 languages that exist in the world. But all these languages are used by humans only and in this digital world, only humans are not interested in communicating with each other only, since the development of machines. And the start of the digital world is because of computer invention. Today we all can work with the computer very efficiently.
But in the beginning, the computer was vast and very fast as today, because it was not able to process our language so it landed to miscommunication. This is time when Natural language processing comes into the picture..