Deploying Al-Based Models? Use Hugging Face Spaces and Render

Hugging Face Spaces and Render are two key cloud platforms that support the development and deployment of Al-based models. Find out how...

- Dr Gaurav Kumar

Artificial intelligence (AI) is the buzzword today with AI-based applications demonstrating great performance, speed, and accuracy.

Their deployment is widespread in domains like healthcare, finance, retail, automotive, manufacturing, logistics, education, agriculture, telecom, travel, insurance, etc (the list includes almost every major field of work).

AI-based models are being trained with datasets and used for predictive analytics, data engineering and many high-performance applications. These include health diagnostics, finance fraud detection, recommendation systems, autonomous cars, smart grids, route optimisation, crop monitoring, network optimisation, customer segmentation, price prediction, threat detection, chatbots, risk assessment, gaming, data analysis, and many other real world applications.

There are several libraries associated with Python for developing and deploying AI models. Table 1 lists the key libraries and packages required for programming AI based applications.

A number of cloud platforms are also available for the development and deployment of AI models and large language models (LLMs) so that these can be accessed on multiple interfaces, including web-based and smartphone applications. Two key platforms are discussed here.

Hugging Face Spaces with Gradio