Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.

Here’s an easy-to-understand and straight-to-the-point guide to drag and drop in Selenium.

When it comes to ensuring software quality, having a well-defined Test Plan and Test Strategy is crucial. As a software developer or QA professional, understanding the differences between these two concepts and how to write a Test Plan or execute a test strategy effectively can significantly improve your testing process.

Epicor’s ability to provide industry-focused, scalable, and customizable ERP solutions has made it a popular choice for organizations across the globe. Epicor’s built-in reporting capabilities are useful for standard reports but can be limiting for organizations that require more advanced analytics. Without deep technical knowledge of Epicor’s data structures, attempting to manually create custom reports can create serious roadblocks to data trust within your organization.

Selenium IDE is a popular open-source tool for recording and running automated browser tests. You can use it to interact with a browser the way a human would, doing things like navigating to URLs, filling in forms, and clicking on links and buttons. You can also use “assertions” to verify things are working as expected, like checking that elements are present, text is present, inputs contain certain values, et cetera.

Companies that stand still in today’s ever-evolving, fast-paced business landscape are at significant risk of losing competitive advantage. Increasingly, high-quality software is playing a vital role in ensuring that organizations are able to innovate, pivot, and adapt to suit the growing demands of their customer base. However, application testing challenges have significantly hindered the pace at which software can be delivered for too long.

For the last couple of years, Retrieval-Augmented Generation (RAG) architectures have become a rising trend for AI-based applications. Generally speaking, RAG offers a solution to some of the limitations in traditional generative AI models, such as accuracy and hallucinations, allowing companies to create more contextually relevant AI applications.

AI Adoption in SMBs: Key Trends, Benefits, and Challenges from 100+ Companies With larger competitors already using AI to streamline operations and gain a competitive edge, SMBs can’t afford to fall behind. But for many, adopting AI is easier said than done. Limited budgets, lack of in-house expertise, and the fear of wasting time and resources on the wrong tools often leave business owners stuck in decision paralysis.