AI Technology Stack: A Guide

An AI tech stack is the foundation of any AI project. But what are they, what components drive them, and how can organizations choose the stacks that best suit their AI needs?

Everyone hears a lot about AI in the media these days. This is why organizations are currently looking at implementing the best generative AI services they can into their ways of working – either from scratch or through third parties.

But that is not all. Organizations are also trying to do the same when it comes to expanding their AI and machine learning services as well.

However, the question many people have outside of the technology sector is what does AI in construction look like? What are the essential components of an AI tech stack? How do you choose the right AI technologies for your tech stack, and how does the choice of an AI tech stack impact scalability and performance?

The following article will answer all these questions for you and the first thing you need to know is that any AI project is based on an AI technology stack. But what are these stacks and what exactly do they do?

Read also: How to use AI in mobile app development

AI tech stack: layers

An AI technology stack encompasses the layers of technologies used to build and deploy artificial intelligence systems. It includes programming languages, machine learning frameworks, data processing tools, and deployment environments that facilitate the development and scaling of AI applications.

But what are these layers and what do they look like in practice? Find out more below:

• Application layer: This is the main part of the stack behind any software application. This layer oversees any application user interface (UI), user experience (UX), front-end creation, application accessibility, and more.
• Back-end layer: This is the part of the stack that handles the servers that drive everything forward. This means it handles the logic governing key application processes, including security, API setup and maintenance, database connection, and application confirmation processes.
• Data layer: All these processes, as well as the business data that helps any app run smoothly, require storage to work properly in any stack. This is where the data layer of any tech stack enters the equation as it governs storage, retrieval, backup, and the overall management of data.
• Operational layer: Finally, the operational layer handles the maintenance, updates, automation, or optimization tasks for any DevOps team. This, in turn, enables these teams to focus on business-critical tasks and produce the best AI experience possible for their clients or customers.

It is important to note that these layers are just the beginning when it comes to an AI stack. There are others, but for the sake of brevity, this article will draw the line on explaining layers here. However, it is worth investigating the AI layer – this layer is becoming more important in AI tech stacks as this technology becomes increasingly prevalent.

AI tech stack: components

There are three key components that come together to ensure AI tech stacks deliver what their users want. These components focus on infrastructure and model building, as well as application deployment and maintenance. Each of these components has its own set of roles to ensure the stack works properly. More information about these components can be found below:

• Infrastructure: This component focuses on data ingestion and preprocessing, as well as selecting and engineering the right features to create a stack that best suits the needs of the organization building it. Essentially, this component pulls data from sources and organizes it into formats the stack’s machine learning model can use to train itself and use post deployment. Additionally, it identifies the right data parameters the machine learning model should operate under.
• Model: This component focuses on training, evaluating and updating the machine learning model driving the stack when necessary. It also saves older tech stack versions in case the organization wants to refer to them in later models for any reason.
• Application: Finally, this component concerns itself with the deployment, monitoring and maintenance of the machine learning model in a post deployment world.

So far, this article has explored what an AI tech stack is and what layers and components drive them forward. However, now we come to the main question any organization has when it comes to implementing any innovative technology into their way of working. How do I know which version or provider of this technology works best for me? Find out more below.

How to choose the best stack for you?

Below are the key points you need to keep in mind when it comes to deciding which technology stack or provider works for you.

• Analyze the programming language closely: The language used to develop key stack components – including integration and source code – must be able to work with the language or languages you use on a daily basis. Otherwise, even though the provider might look perfect on paper, because they are using a tech stack that does not mesh with your setup, you, and your shareholders, might get poorer business outcomes than you expect from a venture like this.
• Scrutinize model providers carefully: The provider you are considering should be able to provide embedding and foundation models through interference endpoints or other means as these models are crucial in developing any Gen AI model.
• Seek out integration simplification: Any provider that seeks to make its clients tech stack journey easier should have a library of proven methods and integration packages on hand, including tools that can create, modify, and manipulate LLM prompts, enabling them to be built for a variety of different purposes at speed.
• Robustness of a database: Any good tech stack provider will offer a proven data storage solution for transactional and operational data, while also offering a vector for embedding data.
• Possibilities of monitoring and evaluating performance: AI tech stack providers should make it easier for you to track the performance and reliability of your new AI model, while also offering analytics and alerts to improve operations where necessary.
• Handling deployment: A good AI provider should be able to scale with you as needed and know how to integrate with your own infrastructure at speed. But that’s not all. They should also be able to tell you what role the cloud can play in the lifespan of your own tech stack while briefing you on the security challenges that come with these stacks and take steps to overcome them for you before they become an issue.

Developing an AI tech stack with Software Mind

At Software Mind we know that developing an AI tech stack is easier said than done, and we also know that undertaking this kind of work can be extremely daunting.

That is where our experienced software experts come in. They can help choose the right development approach for you quickly and easily by connecting with you to understand more about what you need to leverage your new tech stack for, which in turn will save you considerable time and money overall.

So, what are you waiting for? Our experienced software development team is happy to talk about what a properly implemented tech stack can do for you wherever you are.