Corporate Blog: Artificial Intelligence (Page 3)

This article is a step-by-step guide on how to hire the right AI/ML developer for your project. It covers the important skills needed, how to find and interview candidates, and tips for successfully bringing them onto your team.

This article explains what generative AI is and how it can help businesses. It covers the different benefits, real-world uses in various scenarios, and the challenges businesses might face when using it.

Is having an AI app another hype or a necessity? This article explores whether investing in building an AI app is a worthwhile decision for businesses by examining the benefits, challenges, and potential ROI.

This article breaks down the process of building Large Language Models (LLMs), from collecting and processing data to training and applying these powerful AI systems. It also covers real-world uses, common challenges, and future trends in LLM technology.

This article shows you how to add AI and ML to your .NET apps. It breaks down everything from building and adding models to testing your smart new features, so you can easily upgrade your projects.

This article breaks down the basics of prompt engineering for AI, including different types of prompts, cost-saving tips, and how to get clear and precise answers.

This article compares the advantages of using local large language models versus cloud-based services like ChatGPT, Claude, and Gemini, focusing on the cost implications and performance metrics. It provides a detailed analysis of pricing, hardware requirements, and potential savings.

This article provides a comprehensive guide to Product Information Management (PIM) and Master Data Management (MDM), detailing their definitions, purposes, key differences, integration benefits, and future trends.

This article explores the challenges neobanks face in adhering to Anti-Money Laundering (AML) regulations, while also exploring the opportunities available for them to excel in this area.

This article explores how combining artificial neural networks with computer vision techniques can improve pattern recognition in various tasks and applications.