How MongoDB is Helping to Overcome Challenges with AI

As artificial intelligence (AI) and related technologies have become more prevalent, the challenges of working with them have also increased. 

Developers contend with a variety of issues when building AI and machine learning systems. In particular, many find it difficult to obtain the high-quality, labeled data needed to train these systems. Others have trouble deploying AI models to production due to the lack of infrastructure and resources. Additionally, once a system is up and running, developers must continuously monitor and optimize it to ensure that it continues to function as intended. 

The overall process of building AI is an expensive and time-consuming task, and is often reserved for developers with highly specialized skills. In an era where hyper-personalization, automation, and predictive functions are defining business sucess, the pressure for businesses to adopt AI rapidly has never been greater.

Fortunately, MongoDB is helping to overcome these challenges with serverless architecture. It is a cloud-computing execution model in which the cloud provider runs the server, and the customer pays only for the resources used during specific periods of time, such as per request, or per second.  

Such technology makes it possible to deploy AI models without having to worry about the underlying infrastructure. This allows for more agility in the development process and leaves more room to create complex models, as the server management tasks fall on the cloud provider.

​​MongoDB’s Vivek Bhalla iterates developers’ constant struggle when it comes to updating APIs without a guarantee for backwards compatibility. 

He said, “All software vendors have tried their best to ensure each release is backward-compatible, while also adding new features. However, even with this intention planned from the outset, breaking compatibility has sometimes been unavoidable in order to fix specific issues or deliver new capabilities.

MongoDB has helped solve this pain point by enabling seamless upgrades while protecting previous work. To make updates and patches, you can upload bits of code without affecting the whole application.

This is possible through the serveless’s decentralization approach, in which each function of a software runs individually. The entire application need not be running, especially when only several components are servicing user requests.

These capabilities also offer a cost-effective solution to maintain AI and machine learning systems. AI typically comes with expenses only the biggest tech companies can afford. For instance, Wired estimates the cost of developing the GPT-3 model to be around $5 million. And on average, training AI to do specific tasks can cost more than $50,000.

Traditionally, developers are charged for server use, regardless of how often AI models are trained. But cloud providers charge on a per execution basis, saving you money for unused server time and counting only the number of API calls you make. 

Overall, MongoDB recognizes the common obstacles developers deal with when building AI-powered applications, which is why its serverless architecture aims to solve scalability, flexibility, production costs, and productivitiy. Going serverless helps developers navigate these pitfalls and also opens up opportunities for them to focus on more complex development tasks.

While serverless computing in itself still has  limitations, the potential for revolutionizing the way we build AI applications is promising. With more companies adopting the technology, it remains to be seen how it will shape the future of the AI market.

Blog from Kirsten M. Gonzales

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