Learn How to Build AI Software in Five Easy Steps (+ Additional Tips)

15-Second Summary: The entry-level barriers for building AI software can be low but the cost of developing an inefficient product is too high. This article answers the most critical questions about how to create an AI software system from scratch, plus some additional facts and insights to help you get started.

Since AI has become ubiquitous, it's hard not to take notice.

We’ve seen how Tesla is using AI to revolutionize autonomous driving while the local healthcare clinic is implementing AI-powered appointment scheduling.

As it happens, 83% of global organizations have stated that AI is their top priority to accelerate innovation in their business processes.

All this makes you wonder how it works and whether your business should join the AI bandwagon. Maybe you’re feeling left out, or the benefits are too tempting to ignore.

Either way, you’re here, asking yourself – how do I even start developing AI software?

It's a big step, and you want to get it right. With so much at stake – time, money, reputation – you need to be sure.

Let Us Help You Make Winning Business Decisions with Advanced AI Solutions

Get Set, Go

After taking on dozens of AI projects at Radixweb, we’ve learned a thing or two about AI system development. And now, drawing from all that experience, we’re here to show how you can do it, too.

You'll learn about the key phases, best resources, and a lot more (only if you stick around till the end).

Here we go!

Reasons to Build AI Software

Irrespective of the industry, any company can benefit from an AI-powered software system. Whether it’s a startup, SME, or large enterprise, AI can address a number of issues, as explained in the figure below:

So, if you’re wondering why you should build AI software solutions in the first place, take a look at how they offer some of the most critical business benefits:

Tools and Resources You Need for AI Software Development

The choice of tools and technologies is absolutely important for creating AI software. Without the right resources in hand, even the best idea can fall apart. The tools you use will make all the difference.

Having said that, here’s a list of all the essential technologies to build an AI system:

Programming Languages

• Python - This is the most popular language for AI development because it’s easy to learn and has a lot of pre-built tools.
• R - Great for statistics and data analysis, often used in data science.
• Java - Used in large-scale apps and AI models that need to run efficiently.

Frameworks and Libraries

• TensorFlow - An open-source library from Google for building and training AI models.
• PyTorch - Another popular library known for being more flexible and easier to use.
• Scikit-learn - A Python-based tool for data mining and data analysis.

Data Sources

• Datasets - Public datasets are available online, like ImageNet for images and the UCI Machine Learning Repository.
• APIs - These allow you to access data from other services, like Google Maps or weather information.

Cloud Services

• Microsoft Azure - Offers Azure Machine Learning for building and deploying AI solutions.
• Amazon Web Services (AWS) - Tools like SageMaker help in training and building AI models.
• Google Cloud - Provides AI and ML tools like TensorFlow Extended (TFX) and Vertex AI.

Version Control Systems

• Git - Platforms like GitHub and GitLab host repositories online. Teams can track changes in code and manage different versions of the project.

Data Visualization Tools

• Tableau - A powerful tool for creating interactive and shareable dashboards.
• Matplotlib - A Python plotting library that offers a range of static, animated, and interactive plots.

Natural Language Processing (NLP) Libraries

• NLTK (Natural Language Toolkit) - A comprehensive library for building Python programs to work with human language data.
• spaCy - An open-source library for advanced NLP in Python, effective for its performance and usability

Build a Smarter and More Innovative Business Ecosystem with Software Integrated with Cutting-Edge Functionalities

Tell Us Your Requirements

The Step-by-Step Process to Develop an AI Software System

According to a recent survey conducted by IBM, 59% of AI professionals are actively deploying AI while 40% of organizations are stuck in the exploration and experimentation phase due to development challenges.

Without a doubt, AI software development needs both technical and operational proficiency for an efficient, secure, and goal-oriented solution.

In order to help you, we’re going to talk about an eight-stage framework that developers at Radixweb follow for the best possible outcomes. Take a look:

1. Define the Problem Statement

AI can solve a range of problems. What do you want to work on?

Identifying the business case stage sets the foundation for the entire process. Your task here is to clearly identify the problem that the AI system aims to solve.

Let's understand with a simple example.

Assume you’re running an eCommerce store, and you notice a significant drop in sales during holidays or weekends. Here's how you should find the value proposition of AI software implementation:

Get to the Root Cause: Through data analysis and user feedback, you discover that customers often abandon their carts due to long checkout processes or unclear product descriptions.

Explore Solutions: You realize that streamlining the checkout process and improving product information could potentially address these issues and boost sales.

Create the Problem Statement: Your problem statement changes from "sales are dropping" to "a high cart abandonment rate” due to the above-mentioned reasons - something an AI system can solve.

If you find it difficult to find a problem statement for the AI software, you might talk to an experienced software consulting team.

2. Collect and Prepare Your Data Estate

Data is the lifeblood of AI systems. Usually, they need at least 10 examples for each feature (the 10x rule). Obtaining high-quality data is essential for building and training accurate models.

In this stage, you need to collect structured (texts, spreadsheets, records) and unstructured data (images, audio) from as many sources as you can. There are existing datasets to fetch from or you can gather new data through surveys, experiments, or data scraping techniques.

The next steps are:

Clean Data

Raw data is often messy and full of errors, inconsistencies, or missing values. Cleaning the data means preprocessing and transforming it to ensure its quality and integrity.

Feature Engineering

After cleaning the data, it’s time for feature engineering - selecting, extracting, and creating meaningful features from the raw data that the AI models can use to make predictions or classifications.

In this regard, using data pipelines makes the whole process a lot easier, as companies have reported processing millions of data files in just milliseconds.

Here's how a robust data pipeline architecture from Google Cloud looks like:

3. Select the Right Algorithm(s)

The choice of algorithms depends on the type of software development, whether it’s based on generative models, deep learning, or machine learning.

Understand the Problem Type

• Classification: If you need to categorize data into different groups (like spam vs. non-spam emails).
• Regression: If you need to predict a continuous outcome (like house prices based on various features).
• Clustering: If you need to group similar data points together without predefined labels (like customer segmentation), it's clustering.
• Recommendation: If you want to suggest items based on user behavior (like Netflix recommending movies).

Consider the Data

• Size of Data: Some algorithms work better with large datasets (like deep learning), while others are suitable for smaller datasets (like decision trees).
• Type of Data: Text data might require NLP algorithms, whereas image data might need CNNs.

Evaluate Algorithm Characteristics

• Accuracy vs. Speed: Some algorithms are highly accurate but slow (like ensemble methods), while others are faster but less accurate (like linear regression).
• Interpretability: Algorithms like decision trees are easy to understand and explain, while others (like deep neural networks) are more like "black boxes."

Work with Developers Who’ll Help You Win the Competition Curve with Next-Gen Software Solutions

Let’s Connect

4. Build and Train the AI Model

Building and training your AI model is possibly the most important phase of the AI software development life cycle. This is where your selected algorithm learns from the data you’ve prepared and becomes capable of making predictions or decisions.

Here’s how it works, in simple terms:

5. Integrate the AI Model into the Software

After training and testing the AI model, it's time to make it accessible and functional for end-users.

One common method is to create an Application Programming Interface (API) that allows different software components to communicate with each other. You can host your AI model on a server, and the software can make requests to it to get predictions.

Alternatively, for some applications, you might embed the model directly into the software, especially in mobile apps or offline applications that need real-time predictions without internet access.

In this context, you must make sure the AI functionalities have been integrated perfectly into the user interface.

For example, if you're adding a recommendation engine to an eCommerce site, users should see the recommendations displayed on relevant pages.

Challenges You Might Face While Developing AI Software

While the promise of AI-based software development is transformative, it’s essential to recognize that with every great innovation comes greater challenges.

Here are some common challenges you might encounter along the way:

1. Gathering and preparing high-quality data is the real challenge. It needs to be clean, relevant, and sufficient for training your AI model.

2. AI models can inadvertently learn and perpetuate biases present in the data. Be careful with processing unfair or discriminatory outcomes.

3. Another challenge is resource constraint. AI software requires significant computational resources, including powerful hardware and large amounts of data storage.

4. For complex deep learning models that operate like black boxes, understanding how AI makes decisions can be difficult.

What is the Cost of AI Software Development?

Contrary to the common assumption, the cost to build AI software isn’t as expensive as one might think. Yes, it costs more than conventional technologies and general-purpose products, but it’s not beyond reach for even small and medium-sized firms.

Anyway, the final cost hinges on multiple factors and can vary based on your project requirements.

Here's an estimated breakdown of the overall cost and how each factor contributes to it:

Total Estimated Cost Range: $130,000 - $500,000+

Things to Consider for a Successful AI Software System

The recent advancements in AI use cases are truly transformative. It hasn’t stopped impressing businesses and individuals alike with its potential to analyze, predict, automate, and simplify our lives. It’s no wonder so many are eager to take the plunge into the AI software development process.

However, before you start, it’s crucial to consider some key aspects to ensure your AI software is effective, ethical, and valuable. Here are the essential questions to guide you through this process:

Data Quality

• Have you ensured your data is clean, accurate, and relevant?
• Are there any missing values or outliers that could affect your model’s performance?

Bias and Fairness

• Have you checked for biases in your training data?
• What steps have you taken to mitigate any potential bias?

Privacy and Security

• How are you protecting user data?
• Are you compliant with data protection regulations like GDPR?
• Have you encrypted and anonymized the data?

Scalability

• Can your AI system handle increasing amounts of data and users?
• Have you designed your infrastructure to scale efficiently as demand grows?

Ethical Considerations

• Have you considered the ethical implications of your AI system?
• Is your AI being used responsibly without causing harm?

The Future Belongs to Those Who Embrace Change. This is Your Opportunity to Leverage the Limitless Power of AI

Get Me Started

Your Ultimatum for Building AI Software SolutionsMany have tried their shot at creating AI software, but only a few have truly succeeded. Developing such cutting-edge solutions is a significant investment right from the start.Throughout this process, your team will need to invest in advanced technologies, ensure efficient management of organizational data, and train your team to work with AI systems.Feeling overwhelmed? That's completely normal. The process is complex and can seem daunting. If you're not quite ready to take the plunge on your own, consider working with a seasoned AI software dev team. Starting small with expert help can lead to significant achievements down the road.At Radixweb, we can guide you through every step. Whether you need full development services or just a bit of expert advice, we're here to support your idea.Let's talk?