AI Development Life Cycle: A Comprehensive Guide

Updated Apr 9, 2025

The time has come to admit that artificial intelligence is here to stay, with its previously unknown tech opportunities and creativity. That’s why many businesses require a clear understanding of how to develop AI projects and benefit from them. In this article, we zoom in on the AI software development process to help you effectively adopt AI technologies into your company.

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What Is the AI Development Life Cycle?

Just like any software development life cycle, the AI creation process follows an established structure. First, you should define the problem, outlining objectives and constraints. The next step is specific for artificial intelligence: you should collect and prepare high-quality, relevant data for training AI models. Remember that the accurate functioning of intelligent solutions depends entirely on the data quality. Then follow traditional technical stages — design, training, testing, deployment, and of course maintenance.

Key Stages of the AI Software Development Life Cycle

Now let’s discuss each stage in detail.

Problem definition and requirement analysis

At the initial stage, business analysts and project managers set the foundation for future success. The first critical task is to outline the scope of the challenge and define limitations. It’s highly recommended to engage business executives, investors, end users, and other relevant stakeholders in order to collect all the requirements and create a better vision of the future product.

Another important aspect is the evaluation of the solution’s feasibility — technical, operational, and economic. You should also define measurable KPIs at the beginning to be able to assess project’s success at the end.

What sets artificial intelligence and machine learning projects apart from other technologies is the necessity to assess their ethical impact. You should eliminate societal effects and biases and make sure your solution functions according to current AI regulations.

Data collection and preparation

AI systems learn from the data people provide, hence data acquisition should be conducted with meticulous care. If you collect irrelevant or inadequate data and train models on it, your AI solution will be ineffective.

The objective here is to identify multiple data sources (enterprise and proprietary data, public datasets, sensor and connected devices, etc.) and utilize appropriate methods and techniques (web scraping and crawling, APIs and third-party integrations, etc.) to gather raw data.

Once the data is collected, you should properly prepare it before training the intelligent model.

Cleaning: Identify and fix missing values, outliers, duplicates, and inconsistencies.
Integrating: Merge data from multiple sources into a unified dataset.
Transforming: Structure data into the format convenient for your AI system.
Labeling: Add informative labels to provide context for collected data.
Versioning: Track changes and maintain version control.

Model selection and design

There are different types and classifications of artificial intelligence and machine learning models: supervised and unsupervised learning, large language models (LLMs) and knowledge graphs, generative and discriminative models, etc. AI engineers should select the most appropriate model depending on the problem they are going to solve.

Then, experts design the architecture, keeping in mind layer types, connectivity, interoperability, security measures, and other essential parameters.

Model training and testing

The next step is to feed the prepared data into the developed model. During this stage, the model identifies patterns and data relationships, gradually enhancing its ability to make accurate predictions or informed decisions. This process is repeated until the model reaches a desired level of accuracy.

Model testing is the process of validating the trained AI model against a different dataset it has not previously seen to guarantee it can make accurate predictions in new scenarios. This phase uses performance metrics (accuracy, precision, recall, F1 score) and techniques (cross-validation, robustness testing, bias assessment) to provide reliability, fairness, and real-world readiness. If the results are unsatisfactory, the model is refined through further training or adjustments.

Deployment and integration

After successful training and evaluation, the AI model is deployed into a production environment where it starts processing real-time or batch data. Choosing the right deployment strategy — whether cloud, on-premises, or edge — is crucial and depends on the project’s needs.

It’s also important to integrate the model seamlessly with existing systems, plan for scalability, and maintain version control for safe updates or rollbacks. Before going live, thorough integration testing is performed, and detailed documentation is prepared to support long-term maintenance and handover to operational teams.

Monitoring and maintenance

Once an AI model has been deployed, it must be continuously monitored to make sure it performs well over time. This includes monitoring critical parameters, recognizing data drift or anomalous behavior, and determining whether retraining is required.

Feedback loops help update the model by absorbing new data and user interaction, making it more responsive to real-world changes. Security and compliance are also important: ongoing inspections secure the model and make it follow rules.

Key Challenges in AI Development

The AI development process is complicated, so it comes with some challenges you should be aware of.

Data challenges

Data preparation is one of the biggest difficulties when creating AI solutions, because it’s not always easy to acquire a sufficient amount of high-quality, relevant, and consistent data.

In natural language processing (NLP), for example, training chatbots or voice assistants requires large amounts of conversational data. The problem arises with rare languages or regional dialects, as there are not enough books, dictionaries, and audio-visual records to train models properly. As a result, the developed chatbot may perform well in English and other widely spoken languages but struggle with less common ones.

Technical challenges

Computing power, memory, and infrastructure are fundamental for the proper functioning of AI solutions that have to process massive datasets. That’s why engineers must ensure the solution is scalable, performs at its best, and integrates with the existing infrastructure when needed. Maintaining model interpretability is also a growing concern, especially in cases where it’s vital to understand how decisions are made. Continuous optimization and monitoring are also required to prevent model drift and guarantee consistent performance.

Ethical and regulatory challenges

With the growing impact of artificial intelligence on our society, it’s a necessity to protect user privacy, prevent algorithmic bias, and make sure that automated decisions were made in a fair and transparent manner.

For example, AI tools for HR specialists may unintentionally favor certain groups over others if not properly audited. Furthermore, regulatory frameworks require developers to comply with strict guidelines on data usage, accountability, and user rights, adding complexity to the development and deployment process.

Tools and Technologies for AI Development

There is already a rich ecosystem of tools, libraries, frameworks, and environments you can use to create intelligent applications.

Data processing tools

Apache Hadoop: An open-source framework for distributed storage and processing of large datasets.
Apache Spark: An in-memory data processing engine that supports batch and real-time analytics, machine learning, and graph processing.
Pandas: A Python library used for data manipulation and analysis.
NumPy: A Python library for numerical computing that supports multi-dimensional arrays, matrices and mathematical functions.

Machine learning frameworks

TensorFlow: An open-source machine learning framework developed by Google, widely used for building and deploying deep learning models across various platforms.
PyTorch: A flexible and easy-to-use open-source deep learning library developed by Meta (ex. Facebook).
Scikit-learn: A popular Python library for classical machine learning algorithms, offering simple tools for data mining, analysis, and model building.
Keras: A high-level neural networks API written in Python, running on top of TensorFlow, that allows for quick prototyping and easy model building.

Development environments

Google Colab: A cloud-based platform similar to Jupyter Notebook that offers free access to GPUs and TPUs, making it convenient for training and testing machine learning models without local setup.
VS Code (Visual Studio Code): A lightweight, customizable code editor with extensions for Python, Jupyter, and other ML tools.
PyCharm: A powerful IDE developed by JetBrains specifically for Python, offering tools for coding, debugging, and managing ML projects.
IBM Watson Studio: A cloud-based development environment that provides tools for data preparation, model development, and deployment.

Cloud platforms

Google Cloud Platform (GCP): Offers Vertex AI for building, training, and deploying intelligent models.
Amazon Web Services (AWS): Provides comprehensive AI/ML tools through Amazon SageMaker.
Microsoft Azure: Features Azure Machine Learning, a cloud-based platform for developing and managing ML models.

Monitoring and maintenance tools

MLflow: An open-source platform that helps manage the machine learning life cycle.
Prometheus: A powerful open-source monitoring system that collects real-time metrics from applications and infrastructure.
Evidently AI: A tool for monitoring machine learning models. It tracks data drift, target drift, and model performance over time.
WhyLabs: A monitoring platform built for ML systems that provides automated data quality checks, drift detection, and performance tracking.

Best Practices for AI Development

Successful AI initiatives don’t rely on technology alone, they thrive at the intersection of agile execution, diverse collaboration, and responsibility in terms of ethics. Below are three best practices that are foundational to effective AI development.

Agile methodologies

AI models often require multiple rounds of training, testing, and tuning before they perform optimally. That’s why they benefit significantly from Agile practices due to its inherently experimental and iterative nature.

Key Agile practices for AI

Break AI projects into smaller sprints with clear goals (e.g., data preprocessing, model selection, performance evaluation). This helps in regularly assessing progress and pivoting when needed.
Use real-time feedback loops from stakeholders and users to validate model performance and usefulness.
Encourage a fail-fast culture where teams can test different approaches quickly without fear of failure.
Incorporate both model performance tasks and data tasks into the product backlog to maintain a holistic view of AI development.

Cross-functional collaboration

AI development is not just the responsibility of data scientists. It requires tight collaboration across multiple disciplines to succeed.

Why cross-functional teams matter:

Data scientists bring the core modeling expertise.
Engineers deal with the infrastructure, deployment, and scaling.
Product managers align AI capabilities with business needs and user expectations.
Domain experts provide context and check that models make sense in real-world scenarios.
UX designers help integrate AI into interfaces in an intuitive way.
Legal and compliance monitor that regulatory requirements are met.

By breaking silos and encouraging open communication, teams can co-create AI systems that are technically sound, usable, relevant, and compliant.

Ethical AI practices

AI systems influence decisions in many important spheres of life, so ethical responsibility is not optional, it’s essential.

Core principles

Explain the decision-making process of AI systems. Include explainability in model results where possible.
Eliminate bias and harmful stereotypes in training data, algorithms, and outcomes.
Respect user data by implementing strong governance, anonymization, and encryption practices. Follow relevant data protection regulations (e.g., GDPR, HIPAA).
Establish clear lines of responsibility for model behavior, including maintenance and error-handling protocols.
In use cases with high stakes, adhere to human control to avoid unforeseen outcomes.

Practical Applications of Artificial Intelligence

Let’s see how people use AI solutions in different spheres.

Healthcare

Early disease detection (e.g., Cancer, Alzheimer’s)

IBM Watson and Google’s DeepMind are the examples of AI models that can analyze medical imaging (X-rays, MRIs) and find anomalies with greater accuracy and quicker than human radiologists.

Personalized treatment

AI systems analyze patient data and suggest customized treatment plans, improving outcomes and reducing unnecessary procedures (e.g., Tempus or PathAI).

Virtual health assistants and chatbots

Ada and Babylon Health are the representatives of bots that can answer medical questions, manage appointments, and track symptoms.

Predictive analytics in hospitals

AI predicts patient admission rates, potential ICU cases, and needed resources, helping with hospital workflow optimization.

Finance

Fraud detection and prevention

Financial institutions use intelligent algorithms to monitor transactions in real time and detect strange patterns or behaviors suggesting fraud.

Algorithmic trading

AI-driven trading bots execute trades based on predictive analytics and market data, often milliseconds faster than human traders (e.g., Renaissance Technologies, Sentient Technologies).

Credit scoring beyond traditional metrics

AI analyzes alternative data (e.g., mobile phone usage or social behavior) for creditworthiness, helping banks assess underbanked populations.

Robo-advisors for personal finance

Betterment and Wealthfront are the tools that use AI to manage and optimize investment portfolios based on user goals and risk tolerance.

Retail

Personalized shopping experiences

AI tailors product recommendations based on browsing behavior, purchase history, and user preferences (e.g., Amazon, Netflix for retail media).

Inventory and demand forecasting

AI helps predict inventory needs and demand trends to avoid overstock or stockouts (e.g., Walmart and H&M use AI for managing supply chains).

Visual search and augmented shopping

Customers can upload an image and find similar products online instantly (e.g., ASOS, Pinterest Lens).

AI-powered customer service

General queries from customers are processed by virtual agents and chatbots (e.g., Zendesk AI, Salesforce Einstein) much quicker than by human employees.

Manufacturing

Predictive maintenance

AI predicts equipment failures before they happen by analyzing sensor data, minimizing downtime and repair costs (e.g., Siemens, GE Predix).

Quality control

Systems based on computer vision check products for defects on the assembly line with more accuracy than manual inspection (e.g., Landing AI, Instrumental).

Smart robotics and cobots

AI-powered collaborative robots (cobots) work alongside humans, adapting in real time to assist in tasks like welding, assembly, or packaging (e.g., Universal Robots).

Supply chain management

AI optimizes logistics, route planning, and supplier selection, making operations smoother and cost-effective.

Case Studies by SaM Solutions

Our team has successfully developed and implemented numerous innovative projects powered by AI and ML technologies. Here are some examples.

Self-checkout kiosk for public catering facilities

We created a smart self-service checkout kiosk that replaces human cashiers, using computer vision and machine learning to quickly scan and identify food items. It processes cashless payments via QR codes, NFC, RFID cards, and contactless tokens, reducing wait times from minutes to seconds. The kiosk is ideal for restaurants, healthcare, hospitality, education, and retail.

Computer vision solution for the printing industry

A leading provider of printing services required a smart system to ensure quality control and reduce downtime in production. Our experts created a system based on computer vision that recognizes products, analyzes production line failures in real time, and delivers alerts.

Smart business assistant

The client from the manufacturing industry turned to SaM Solutions with the request to develop an intelligent business assistant. Our team created a tool based on natural language processing (NLP) that interprets various data from business processes. Users can interact with the solution in everyday language.

Current Trends in AI Development

As AI continues to expand, so do the tools and techniques. Here are some emerging trends in the sphere.

Federated learning

What it is

Federated learning is a machine learning approach when you train models across multiple decentralized devices or servers holding local data, so the data is not transferred to a central server.

Why it matters

Data stays on the user’s device (e.g., phones, hospitals, IoT devices), making it ideal for sensitive use cases.
Processing happens closer to the data source, enabling faster responses in real-time applications.
Organizations can collaborate on model training without exposing proprietary or regulated data.

Example: Google uses federated learning in Gboard to improve predictive text suggestions without pulling users’ keyboard data into the cloud.

Explainable AI (XAI)

What it is

Explainable AI is a set of techniques and tools that make AI model decisions understandable to humans.

Why it matters

Users and stakeholders are more likely to adopt AI if they understand how and why decisions are made.
In banking, healthcare, and other sectors laws may require justification for automated decisions (e.g., loan approvals or diagnoses).
Developers can spot flaws, bias, or unexpected behavior more easily with explainable models.

Example tools: LIME, SHAP, and integrated gradient techniques help visualize which features influence a model’s predictions.

AutoML

What it is

AutoML is the process of automating the end-to-end tasks of developing machine learning models, including data preprocessing, model selection, hyperparameter tuning, and even deployment.

Why it matters

Non-experts can build effective models, which expands access to AI across business units.
Experimentation and model development cycles become faster.
The best-performing models can be found through automated search, often outperforming manually crafted solutions in less time.

Popular platforms: Google AutoML, H2O.ai, Amazon SageMaker Autopilot, and Microsoft Azure AutoML.

The Future of AI Development

Intelligent technologies will become smarter and more sophisticated year by year, so their future seems promising.

Integration with emerging technologies

Artificial intelligence will be tightly connected with other advanced technologies, including blockchain, IoT, cloud computing, 5G, and more. According to a recent report by PwC, AI’s contribution to global GDP could reach $15.7 trillion by 2030, with most of this growth driven by automation and enhanced connectivity.

Real-time AI life cycle monitoring

It’s becoming increasingly important to continuously monitor the AI development process. On the one hand, it results in the most accurate outcomes. On the other hand, it gives the opportunity to eliminate issues before they cause negative effects.

Focus on ethical and responsible AI

As an example, over 60% of AI-driven hiring tools currently exhibit bias, highlighting the need for transparency and fairness. According to a survey by Deloitte, 73% of organizations are now prioritizing ethical considerations in their AI strategies. These advancements promise a future where AI is not only smarter but also more transparent and aligned with societal values.

Why Choose SaM Solutions for AI Software Development?

Our company has over 30 years of experience in delivering tailored solutions across various industries. We leverage the latest advancements in machine learning and data analytics to create impactful AI applications that drive efficiency, automation, and long-term growth. Our AI services include rapid prototyping, seamless integration with existing systems, and ongoing support. SaM Solutions offers flexible collaboration models, including full-cycle project development, solution architecture guidance, and team augmentation.

Conclusion

Are you planning to build an AI application or integrate intelligent functions into your existing business infrastructure? Feel free to contact SaM Solutions experts to discuss your ideas and prepare an action plan.

FAQ

How do you choose the right team for implementing an artificial intelligence project?

At SaM Solutions, we have an established, thorough recruitment process that identifies individuals with diverse yet complementary skill sets. Our large pool of talent ensures that you can select team members who align with your project’s specific needs and goals.

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About the Author

Natallia Sakovich

IT INDUSTRY OBSERVER

At the forefront of the tech industry since 2017, Natallia is devoted to her motto – to write about complicated things in an easily comprehensible manner. With her passion for writing as well as excellent research and interviewing skills, she shares valuable knowledge on various IT trends.

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