How to Create an AI System in 5 Steps

The latest McKinsey research claims that 78 percent of responding organizations use AI in at least one business function, up from 72 percent in early 2024 and 55 percent a year earlier. At the same time, businesses often require individual solutions to achieve end-to-end automation of complex processes. If this is a challenge for you, please read the insights on how to code AI and make artificial intelligence inspired by our professional experience below.

Why Should One Consider Investing in AI Software?

Today's market is overflowing with ready-made AI products – so, how to understand whether there is any sense in custom AI development?

Investing in AI Software Brings a Lot of Advantages

Against numerous ready-made SaaS and API AI solutions, as well as no-/low-code platforms (their how-to-use guides are sufficient to understand how to make an AI on your computer), custom AI development still makes sense.

For businesses with unique data, business rules, and/or non-standard operational logic, an out-of-the-box solution will require comprehensive adaptation, the cost of which will be higher in the long term than custom development.

Which Programming Languages Are Used in AI Development?

Before we explain how to create an AI from scratch, let’s consider languages suitable for AI development.

1. Python

Python has the most extensive ecosystem of libraries. It is characterized by simple syntax and many of ready-made tools for MLOps, deployment, and serving models. This makes it a one-size-fits-all choice for developing any AI solution, especially for NLP, computer vision, data processing from multidimensional arrays, and Deep Learning.

2. Julia

Julia was created to focus on high-performance numerical calculations and the needs of Data Science tasks. It boasts top performance comparable to C and is worth choosing when creating ML solutions in engineering, bioinformatics, robotics, and quantum computing.

3. R

R was created as a language for statistics, data visualization, and academic research, but as libraries expanded, it became more versatile. Today, R is actively used in sociological and health-tech projects, especially in combination with Python.

How to Create an AI System: 5 Key Steps

AI development is a multi-stage process requiring a well-thought-out architecture and KPIs. Below, we will explain how to make AI through these essential AI steps.

Step 1: Establish a Goal

You risk wasting your budget if you don't understand the business goals and processes that can be optimized with AI.

Why Establishing a Goal Matters

Without a clear goal, custom AI development may be less justified than development based on other, more budget-safe technologies. Therefore, find out why AI can deliver unachievable results by conventional technologies.

Identifying the Market Need

If you build your own AI for a broad audience, you must identify their pain points and needs. Also, analyze the availability/sensitivity of data to make your choice in favor of AI legally justified and identify other limitations, including scalability, audit of decisions made, etc.

Moving Forward

Define what AI has to do and what its success metrics are. Additionally, assess the availability of training data and create a value map with ROI estimation and benefits for end users.

Step 2: Collect and Refine Data

An AI model learns from input data, so the output will be useless or dangerous if it is of low quality (irrelevant, incomplete, or biased).

Why Data Matters

In custom AI development, the final product's accuracy and safety depend on the quality of the training data. Errors include sampling bias, data noise, incorrect labeling, and data leakage.

Types of Data

You should not just aggregate data – it is essential to understand which will help solve your problem. This can be:

• Structured data (tables, databases, CSV, telemetry, etc.);
• Unstructured data (texts, images, audio, video, program code);
• Semi-structured data (data in JSON, XML, HTML formats, as well as event logs).

Understanding Unstructured Data

When working with unstructured data, organize complex pipelines with normalization, cleaning, filtering, parsers, annotators, OCR, data versioning, and NLP preprocessing. It is also important to correctly choose data lineage, deduplication, and versioning tools.

Step 3: Develop the Algorithm

Each task requires a unique approach to code an AI, so even within the same category of tasks (for example, data classification), the results of different models may have different errors.

No One-Size-Fits-All Algorithms

The choice of model depends on the task assigned (e.g. prediction, classification, clustering, etc.), the type of data in the training samples (text, numbers, images, etc.), and the available resources (computing, memory, latency limitations).

Types of Algorithms

Determine the type of algorithm that will form the basis of your custom AI solution. This can be:

• Supervised learning based on labeled data (XGBoost, SVM, and neural networks fall into this category);
• Unsupervised learning to identify unlabeled-data patterns (this can be clustering or PCA);
• Reinforcement learning based on the model’s interaction with the environment;
• Hybrid models combine several approaches to minimize error.

Assessing Suitability

When you create your own AI system, you should also assess its suitability. This can be done by expanding the volume/increasing the data quality in the training set, assessing target metrics (MAE, F1, Precision, Recall, BLEU, etc.), and defining the acceptable error.

Using Pre-Trained Models

Use pre-trained models to reduce your AI-powered solution's time to market. The most common ones are BERT (natural language processing), ResNet (computer vision), Whisper (audio), and CodeBERT (program code generation).

Step 4: Train the Model

Let's move on to the most interesting part of creating an AI model – its training.

Allocate Data for Training

The classic data splitting scenario is "train->validation->test", although in cases with limited data samples, a simpler approach with k-fold cross-validation may be sufficient. Eliminating data leakage is also crucial.

Teach the AI to Identify Patterns

Training AI for pattern identification can be done online when data is transferred in real-time and offline when data is uploaded into the model locally and in batches. Remember to implement hyperparameter optimization to minimize the loss function.

Make Predictions Based on Patterns

Evaluate overfitting and analyze critical metrics such as Precision, Recall, ROC-AUC, and F1. Also, prepare fallback mechanisms so that if the result's accuracy is uncertain, the model can forward the request to a human expert.

Step 5: Deploying the Final Product: Bringing Your AI to Life

This is the last AI step, which involves implementing integrations and introducing tools for quality control of the functioning AI model.

Polishing the Final Details

When you build an AI tool, you have to integrate your AI solution with external systems (for example, via REST/gRPC API) and set up monitoring, logging, and alerting. Another important thing is to implement automatic rollbacks with MLOps to maintain the proper quality of the model.

Defining the User Interface

If the solution is a web application, you can implement the interface through JavaScript or React/Vue. Integration is usually performed through the backend API for mobile and desktop apps.

Building the Brand (If It is a Service)

If you create an AI product as a service, you need to define its value for end users, develop a whitepaper, and come up with use cases. An additional advantage will be the presence of certificates and partnerships.

Our Best Practices for Developing AI Systems

Here is a list of the best practices that answer the question: “How is AI created by the Che IT Group's team?”:

• Focus on data quality. The quality of the input data determines the AI ​​model's accuracy. Since the product owners usually provide the data, we take some measures to improve it.
• Explainability and no bias. For legally controlled niches, such as finance and healthcare, we use interpretable models that explain the decisions they make. Additionally, we ensure GDPR/HIPAA compliance by eliminating unnecessary data collection and implementing federated learning or synthetic data at the development stage.
• MLOps practices and lifecycle automation. When the model prototype is ready, we automate its training through pipelines, implement CI/CD practices, and also establish monitoring of metrics such as data drift, bias, and latency. We can also ensure retraining and versioning.

If these practices to develop AI software are close to you and you would like to implement them in your solution, feel free to contact us to discuss its details and start development.

Challenges of Building an Artificial Intelligence System

When you are going to create an AI model, this task always involves a lot of challenges, especially when it comes to production-grade AI.

For example, insufficient or low-quality training data samples are the most common problem in developing AI. Another challenge is the need for constant scaling of computing resources, which, with the strict requirements for sharing data with third-party organizations, cannot be solved through cloud hosting. Thus, many organizations have to deploy in-house data centers and implement MLOps. In the end, no AI-based solution is autonomous, so without seamless integration with external systems, it cannot solve the assigned tasks.

Conclusion

Now, you know how to create AI and understand that this process always requires a planned software engineering process. Therefore, with the right approach to make an AI model, even a startup with a limited budget can produce a solution that solves problems with high user demand. If you are looking for a team that will bring your business idea to life, feel free to contact us.