The artificial intelligence (AI) market is expanding. Statista has calculated that the number of AI tool users will increase by almost 240% and reach 1.2 billion by the year 2031. Businesses are thinking of ways to enhance their performance with AI potential. Their tech experts may choose to develop an AI system from scratch, as there is a wide choice of tools and platforms, both commercial and open-source that facilitate the process. Another option is to address a software development company with the necessary expertise to achieve the desired results. In this post, we prepared the outline of the main things developers should know before tackling the AI building.
Dmitry Baraishuk, the Chief Innovation Officer (CINO) of a custom software development company, Belitsoft, shares his expertise in AI software development and its best practices.
Belitsoft provides full-cycle LLM training services to advance generative AI applications, ranging from bespoke AI chatbots to complex virtual industry-focussed assistants. Their ML professionals fine-tune LLMs, leveraging proprietary datasets and employing prompt design strategies to ensure the best outcomes.
Types of AI
Today, we distinguish between three types of artificial intelligence. They differ in the complexity and the scope of tasks that they are able to perform. Let’s discuss the theory to know what you are planning to develop and use in your project.
Artificial Narrow Intelligence (ANI)
Developers call this type the Weak AI. ANI carries out particular tasks. For example, it can handle computer vision, playing chess, or natural language processing (NLP). We use ANI while interacting with Siri, Alexa, and recommendation assistants on e-commerce sites. These AI systems don’t generalize knowledge. They rely on special datasets for model training.
Artificial General Intelligence (AGI)
This is a Strong AI. Its potential is to perform any task that a human can tackle. AGI systems demonstrate reasoning, understanding of human language, and complex problem-solving. It can handle a new task without substantial retraining. Besides, it can switch domains.
Artificial Superintelligence (ASI)
ASI is currently in exploration. It is planned to outperform humans in every field. It has the potential to significantly change industries, science, and business processes. However, there are concerns regarding its ethical adoption and responsible implementation. It needs powerful guardrails to make sure it functions within human values.
Which Languages Are Used in AI Development?
The programming languages that we are discussing here contain convenient special tools and have substantial community support, which makes them popular options for developing AI systems.
Python
Python has become the leader in the development landscape. It is simple and readable. There are a variety of useful Python libraries and frameworks designed for machine learning (ML) development. For example, PyTorch, TensorFlow, and spaCy help ML experts build advanced AI models and embed deep learning. Python is used in the domains of NLP, computer vision, and learning reinforcement.
Julia
This one is young and currently gaining popularity. In comparison with Java or C++, its syntax is simpler and it is faster than Python or R. Julia handles large-scale AI software. It offers developers advanced tools for mathematical calculations and simulations. The Flux.jl library is designed to create neural networks.
R
This language has a wide ecosystem of tools that engineers use to model statistics and manage data quality. Besides, R deals well with visualization and analytical tasks. Developers use it for AI research.
Other languages
Apart from the above-mentioned languages, there are also Scala, C++, and Java. They integrate smoothly with no-code AI platforms, which allows for developing hybrid solutions. Java is used in high-scale data processing, robotics, etc.
Selecting the Platform for AI Development
Engineers should consider several factors while choosing a platform for developing an AI. Ask yourself the questions from this list:
- Why do you need AI?
Do you need NLP, computer vision, or speech recognition assistant? Different platforms suit different tasks. For example, Google Cloud AI is strong in NLP and translation features. Developers use Azure ML to create data analytics tools and ML models. AWS AI assists in creating scalable AI software and further integrating it into the cloud environment.
- What do you expect from AI?
Specific functionality usually corresponds to particular use cases. More universal requirements are data security, scalability, and no-code solutions. An AI tool should protect customers’ sensitive data. AWS and Azure provide developers with scaling possibilities. As for the no-code solutions, experts who have insufficient technical knowledge, are interested in platforms like IBM Watson or Google’s AutoML.
- How much are you willing to pay?
You can select a relevant pricing model. The pay-as-you-go approach allows for aligning costs with actual usage of the platform or tools during the development. Various licensing terms for enterprises are also available (e.g., one-time payments, recurring fees, etc.). Besides, there are always hidden expenses for additional computational resources or other tools.
- What are your compatibility requirements?
You may wish it to coordinate with your programming language. Besides, it should match the data cleaning and data analysis workflows and comply with the frameworks and libraries that you use.
- What about the community?
Well-supported platforms with an active community and detailed documentation allow for fast problem-solving and increased speed of development.
Steps of Building an AI System from Scratch
1. Set the goal
AI systems solve certain tasks. It is essential to formulate a task as precisely as possible. In this step, you define how end users will benefit from your solution. Explore the section “Common AI Applications” below if you are unsure about your project.
2. Gather and clean the data
The success of an AI model depends on high-quality data. It should match the domain and be sufficient for dealing with potential scenarios. The data may be structured (datasets, spreadsheets) or unstructured (videos, images, notes, presentation slides, etc.). Data cleaning processes include removing duplicates or biases, completing missing data, standardizing formats, etc.
3. Create an AI Algorithm
The algorithm is the “brain” of your AI solution. It determines how AI is going to process the data and how it will produce relevant outcomes. For example, neural networks are best for deep learning apps. If you are developing a classification tool, K-Nearest Neighbors (KNN) will suit your aims. Random forests help to create complex solutions on the basis of large datasets.
Pre-trained models from Google or OpenAI allow for a faster development process rather than creating the whole algorithm from scratch. Scalable AI software allows developers to handle increased data loads.
4. Train the model
AI solutions perform effectively when they can successfully predict the next word (token) in a sequence. This is possible if they digested sufficient amounts of high-quality training data. Data scientists distinguish between training data (80%) and testing data (20%). At the training stage, a model learns to classify concepts, predict tokens, and detect anomalies.
Training is not a single action with its beginning and ending. It includes a repetition of testing and fine-tuning. When engineers find errors, they iterate the rounds to achieve refined performance.
5. Deploy the system
At this step, engineers integrate the product with the environment. Developers determine the user interface and design the branding for the AI tool. During and after the deployment, the solution should be safe, easy to use, and comply with its objectives.
6. Track and improve
Constant monitoring is essential to understand that the model adjusts to changing conditions. It is important to track the following issues: performance (accuracy metrics, error rates, etc.), user feedback, data updates, security maintenance, and new trends in the AI market.
Common AI Applications
Here are some of the use cases of AI apps.
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- Computer vision is the ability of the software to interpret visual data, such as videos, images, medical scans, etc. The scope of apps includes facial recognition, self-driving cars, medical anomaly detection, sports analytics, etc.
- Customer service uses AI-powered chatbots and agents that support clients. They are available 24/7, which increases customer satisfaction.
- Virtual assistants are Siri, Alexa, and similar ones. They communicate, perform commands, answer questions, and remind of necessary activities.
- Predictive analysis can forecast trends, e.g., in the financial domain. In healthcare, AI predictive analytics helps to instantly react to upcoming disease outbreaks.
- Recommendation algorithms offer products to visitors of e-commerce platforms. Social media feeds, streaming platforms, and websites with movies use similar algorithms.
- Fraud prevention tools analyze users’ behavior and define suspicious and hazardous activities.
- Data mining allows for analyzing and extracting necessary details from large datasets. It helps save time and results in performance optimization.
Best Practices for AI Development
When building an AI from scratch, developers have to possess substantial knowledge and experience. Being experts in AI software development, we provide the following tips that simplify the process:
- Formulate the pain points of the users of your AI software.
- Take data cleaning seriously. Train AI on unbiased and sufficient information.
- Regularly monitor the performance and make relevant improvements if necessary.
- Make sure your AI tool maintains data privacy and security.
- Keep an eye on the latest trend standards, regulations, and requirements. Ensure your project complies with legal demands.
- Document all the steps, including data gathering, pre-training, assessment, etc. Therefore, you will guarantee the reproducibility of the tool.
AI Software Development Costs
The type of the project impacts the budget of building AI. There are several factors that form the price:
- Software complexity: a common AI chatbot is cheaper than a sophisticated deep-learning system.
- Training data: a manual process of gathering, cleansing, and labeling the data contributes to the expenses.
- Infrastructure: powerful computational resources and cloud services are costly.
- Team: experienced ML engineers, data scientists, and developers demand appropriate rates for their expertise.
- Maintenance: upgrading and enhancing the software after the deployment is also an expenditure item.
FAQ
What are the challenges of creating an AI from scratch?
The most common challenges are a lack of expertise among the IT specialists, privacy issues, insufficient data of high-quality, and possible resistance to change from the employees.
How do I count the ROI of AI software implementation?
Companies decide to implement AI in their workflows to achieve some goals. Therefore, they should establish certain key performance indicators (KPIs) to understand to what extent their goals are reached after the AI deployment. These KPIs may include cost savings, revenue growth, etc. Businesses can use tools like Microsoft’s ROI calculator for agentic AI, IDC benchmarks, or BI platforms.
How can I achieve the security of an AI system?
Security is important in AI software development to prevent issues like identity theft or financial data fraud. Cybersecurity experts use such measures as encryption, anomaly detection, behavior analysis, zero-trust architecture, and other risk mitigation approaches. It helps to protect users and their data.
