1. Home
  2. Blog
  3. Technology
  4. How to Develop an AI System in 5 Steps
Technology

How to Develop an AI System in 5 Steps

AI is an emerging technology that’s changing the landscape of the business world. The good news is that building one is not as hard as some people may think.

Joe Lawrence

By Joe Lawrence

As a Principal at BairesDev, Joe Lawrence is transforming industries by leveraging the power of veteran “nearshore" software engineers from Latin America.

10 min read

Featured image

Artificial intelligence, machine learning, deep learning: terms that have grown in popularity in the last ten years. The massive increase in processing power as well as the widespread adoption of cloud computing has given us the tools to build AI capable of doing some of the most amazing tasks imaginable.

From AIs writing papers about themselves to AIs winning art contests, the limits of autonomous systems are tested daily. This has led many to ask themselves how to develop their own AI system. How can I enhance my business with AI It must be hard, right?

Actually, no. Starting from scratch might be extremely difficult (there is a reason why these softwares are built by top-tier engineers). But there are hundreds of tools on the market, both commercial and open source, that are meant to facilitate the process. With the right mental framework, a few guidelines, and a solid plan, you’ll be building an AI in no time.

Which Programming Language Is Used in AI?

Before we delve deeper, we have to talk a bit about the basic foundations of AI, including which programming languages are a better fit for creating your own.

Any robust programming language is perfectly capable of building AI systems, but some of them stand out as the best languages overall. In some cases it’s because the language has AI-friendly functions built in, while in others it’s because the community has gathered around these languages, producing tools to facilitate AI systems. Here is a quick list.

Python

Slice it any way you want, and Python will almost always come on top as one of the most popular programming languages. It’s all-purpose programming and interpreted language that has earned its place for its ease of use, readability, and massive amount of packages, libraries, and frameworks.

Python is a fantastic language for AI, with dozens of tools built to facilitate the process. PyTorch, for example, is a very powerful framework for machine learning that has a friendly and simple interface built on Python (or if you are up to the challenge, C++). It should come as no surprise that this language is a favorite considering that it has been adopted as the go-to for the data science community.

Julia

Out of all the options on this list, Julia is the youngest, and that’s a good thing. Julia was built from the ground up to be a data science language — one that covers most of the limitations of other languages on this list, less syntactically complex than Java or C++ and faster than Python or R.

It’s a language that’s slowly gaining ground in the data science community. And you should be on the lookout if you are interested in emerging technologies.

R

R was the reigning king of data science until Python came along. This open-source alternative to the S language has been a favorite of academia for quite a while. It’s not the easiest to use (or understand), but its plethora of libraries backed by the scientific community is hard to replace.

Other popular languages include Scala, Java, and C++, if anything because of their massive adoption and popularity in and out of the software engineering world. While sometimes dense, these three stand out because of their performance and well-nurtured ecosystem.

What Is Required to Build an AI System?

1. Define a Goal

Before writing your first line of code, you have to define what problem you want to tackle. AIs are trained to solve specific issues, and the less defined your problem, the more difficult it is to build your solution. At this stage, if you are going with your AI as a product, you have to define your value proposition: What is the problem, and why is it a good idea to invest in your product to solve it?

2. Gather and Clean the Data

As I’ve always said, a model is only as good as the data it was created with, so having the right data to train your AI is extremely important. What do we mean by the right data?

  • The data is relevant to the problem you are trying to solve.
  • There is sufficient data to adequately represent all possibilities and outcomes.
  • The data isn’t biased.

Data comes in two broad types: structured and unstructured. Structured data is clearly defined information with simple search parameters — for example, the contents of a spreadsheet. Unstructured data on the other hand is complex and cannot be parsed easily — for example, a transcript from a conversation.

As every data scientist knows, data is hardly ever structured. Most of the time we have to clean it and organize it to make sense of it. That same principle applies to AI. Getting the data ready by ordering it, deleting incomplete entries, and classifying it is called cleaning the data.

3. Create the Algorithm

No two AIs are alike. A language learning model is very different from a perception AI. Neural networks and deep learning, random forests, k-nearest neighbors (KNN), and symbolic regression are some of the mathematical underpinnings of AI, each serving its own function and solving a specific kind of problem.

For example, neural networks are fantastic for predictive models, while KNN is built for classification. The nature of the task and the scope of your project will help you assess what’s the best algorithm for your project.

Some companies like Google offer pretrained models ready to be customized and deployed. These are built with millions of data entries and are more robust than what most of us are capable of accomplishing. Instead of training from zero, you could use one of these services instead.

4. Train the Algorithm

An AI needs to learn its task; this is what we call training. As a standard, most data scientists use 80% of their data set to train their models, and the remaining 20% is used to assert the model’s predictive capabilities. Training means that the AI identifies patterns in the data and makes a prediction based on said patterns.

5. Deploy the Final Product

With the AI trained, it’s time to polish the final details and deploy the product. At this stage, we define the user interface and its scope, and if it’s a service, we build the brand around it.

From the auto industry to common daily tasks, AI is becoming a core technology in almost every field, and with the sudden increase in interest and revenue potential, it’s to be expected that new tools are emerging for developers and non-developers alike to build intelligent systems.

Tags:AI
Joe Lawrence

By Joe Lawrence

As a Principal at BairesDev, Joe Lawrence is transforming industries by leveraging the power of veteran “nearshore" software engineers. As a seasoned cross-industry executive, he knows what it takes to deliver end-to-end, scalable, and high-performing solutions across the full spectrum of modern technologies.

Stay up to dateBusiness, technology, and innovation insights.Written by experts. Delivered weekly.

Related articles

Contact BairesDev
By continuing to use this site, you agree to our cookie policy.