AI/ML

Artificial Intelligence / Machine Learning

What is AI?

AI stands for Artificial Intelligence, which refers to machines being able to make human-like decisions and solve problems through the use of processes and algorithms.

Examples of AI include online chatbots used to provide customer service in place of actual human staff, and recommendation engines found in search portals that show you things related to what you are searching for.

Other examples of AI include Computer Vision, where machines are trained to understand meaningful information from images, videos and other visual input via the use of camera, data and algorithms.

What is ML?

Machine Learning (ML) is a subset of AI where a machine is able to take a data model and improve on it through the use of predictions and fine tuning to align it with provided training data.

Practical applications of ML in real life include the automated stock trading bots that buy and sell based on past data models, or predictive text that shows the most frequently used words when you type in certain alphabets.

Requirements of AI/ML Servers

AI and ML are extremely resource intensive as servers running AI and ML have to process a massive amount of data to achieve human-like understanding. AI and ML require dedicated powerful servers that are able to provide high computational power and GPU accelerators to provide maximum performance quickly.

AI/ML Server Solutions

HarshPro Servers are equipped with powerful high capacity Intel^® Xeon^® processors that can support any required computational needs. The servers are also equipped with high speed NVMe SSDs, and support large amounts of memory, making it the ideal choice for processing or storing large amounts of data.

All HarshPro Servers can be easily customized to fit your specific needs. The HarshPro IP20A is designed to support up to two 2.7 slot width PCIe GPU cards, perfect for deep learning applications like voice and image recognition.

HarshPro Servers are designed for use in industrial environments and can work standalone or clustered in remote locations. The server supports functionality commonly used in data centers including automated provisioning and remote management, suitable whether you need to be on the cloud or on-prem.

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