AI ENGINEERING
Artificial Intelligence Engineering (AI Engineering) is a technical discipline that focuses on the design, development, and implementation of AI systems. This discipline applies engineering principles and methodologies to create scalable, efficient, and reliable models that are used to solve problems with AI-based solutions. AI Engineering combines elements of data engineering and software engineering to create adaptive applications for the real world in a variety of fields, such as healthcare, finance, autonomous systems, and industrial automation. This discipline is vital because it allows us to not only use data and technology effectively, but also to ensure that the systems we create are safe, reliable, and ethically responsible. This ensures that the impact of technology on society is positive and that these technologies help solve not only business challenges or household issues, but also complex global problems.

PROBLEM DEFINITION AND REQUIREMENTS ANALYSIS
The success of AI-Engineering projects begins with a thorough problem definition and requirements analysis. This stage is one of the most important, as it helps to create a solid foundation for the subsequent stages of the project - from model development to its implementation.
1. Understanding the problem and defining the scope.
The first step is a detailed analysis of the client's needs. During this process, the engineer:
Identifies the task: precisely defines what problem needs to be solved, for example, whether it will be classification, prediction, anomaly detection or other AI task.
Sets goals: clearly identifies strategic AI goals that meet the client's business needs. This can include setting goals such as increasing productivity, reducing costs or improving customer experience.
Defines the scope: determines what specific solutions will be provided, what are the project constraints and what results are expected within a given time frame.
2. Business context analysis.
The business context has a significant impact on model development and implementation decisions. AI-Engineering assesses:
Industry specifics: Analyzes industry-specific features, such as healthcare regulations, financial security requirements, or the dynamics of manufacturing processes.
Existing systems: Examines what technologies and processes are already in use in the organization to ensure that the new solution can be easily integrated.
Stakeholder goals: Collaborates with the client to understand the key performance indicators (KPIs) that will be used to measure success.
3. Model development strategies.
AI-Engineering selects a model development methodology based on the project requirements:
Building a model from scratch: Used when the task is unique or when there is no suitable pre-trained model. Requires algorithm selection, architecture development, and preparation of large data sets. More applicable to specific and complex tasks that require a high level of personalization.
Pre-trained model: saves time and resources when an existing model can be adapted. Engineers focus on fine-tuning the model to the specific needs of the client.
It is especially effective when generative models such as GPT or BERT are available, which can be easily adapted to specific tasks.
4. Proposal creation and approval.
After analyzing the requirements and identifying solutions, AI-Engineering prepares a detailed proposal, which:
Describes the project scope, goals and activity plan in detail.
The project budget and timeline are determined.
The client reviews the proposal and provides feedback. After final proposal approval, project implementation planning begins.
5. Benefits and strategic approach.
A methodical approach to problem definition and requirements analysis allows you to:
Ensure that the solution being developed exactly meets the client's needs.
Optimize resources, reduce the time and costs required for project implementation.
Achieve long-term results that contribute to the organization's efficiency, innovation and competitiveness in the market.
This stage provides a solid foundation for the entire project from planning to the implementation of the final product. This allows customers to be confident that their investment in AI solutions will not only pay off, but also provide significant added value.

DATA SCIENCE - THE FOUNDATION FOR ARTIFICIAL INTELLIGENCE SYSTEMS
Data science is a core component of all artificial intelligence (AI) systems, combining statistics, data analysis, and machine learning techniques to extract insights and build models from large and diverse data sets. The discipline builds on a strong foundation of data engineering that ensures data quality, accessibility, and usability, and also includes the following important processes:
Data analysis: This involves applying statistical methods and algorithms to uncover trends, patterns, and correlations in data. Analysis can be applied in a variety of areas, from consumer behavior to financial market forecasting.
Modeling: Using machine learning techniques, data is transformed into models that can predict outcomes or generate recommendations. Modeling is essential for solving complex problems and making decisions in real time.
Data visualization: This is vital for presenting complex data analyses and model insights to stakeholders. Well-designed visualizations help convey complex information clearly and effectively, allowing users to easily understand data insights.
Data quality assurance, management: Ensuring that data is accurate, complete, and reliable is essential for the success of any data science project. This requires continuous monitoring and management of data quality.
Data security and privacy: When storing data, especially personal information, it is essential to adhere to strict security protocols and privacy regulations. This includes data encryption, secure data storage methods, and compliance with legal requirements such as GDPR.
Challenges: The field of data science remains challenged by the handling of large amounts of data, the integration of various data sources, and rapidly changing technologies.
Trends: Recent developments in data science include the application of artificial intelligence algorithms such as deep learning and neural networks, automation, and a growing focus on ethics and data integrity. All of these activities are vital to building reliable and efficient artificial intelligence systems that can effectively respond to changing conditions and provide valuable insights. Advances and developments in data science are opening up new opportunities for companies to improve their operations, increase efficiency, and foster innovation across all areas of their operations.

DATA ACQUISITION AND PREPARATION FOR AI SYSTEMS
Data acquisition and preparation is one of the most important stages, as the performance of any AI system depends on how well the data reflects the problem being solved.
High-quality data is the foundation for successful model training and accuracy.
1. Data acquisition for systems built from scratch.
When building AI systems from scratch, data engineers must ensure that the datasets are:
Comprehensive: The data should reflect all parts of the problem domain, covering a variety of situations and contexts, for example, when building a facial recognition system, data should be collected from different lighting angles and groups of people.
Diverse: Diversity in the data is essential to ensure that the model does not understand data bias and is suitable for application in real-world situations.
Accurate and complete: Handling missing values ​​is critical to avoid errors during model training. Inaccurate or messy data can significantly affect the performance of the model.
Key steps:
Data collection: Data can be collected from a variety of sources, including databases, APIs, sensors, or social networks.
Data cleaning: Noise is removed, missing values ​​are filled in, and messy data is handled according to the specifics of the task.
Data normalization: All data is transformed into a single format to make it easier to process in models.
Augmentation: Data is enriched with new elements, such as by creating synthetic data or applying data augmentation.
2. Data acquisition using pre-trained models.
With pre-trained models, data needs are more specific:
Specific data: Data that is relevant to a specific task is emphasized, such as texts on a specific topic or photos from a specific context.
Quality priority: Since the amount of data is smaller, its quality becomes extremely important.
Advantages:
Smaller data: Often smaller but highly targeted data sets are sufficient.
Faster preparation: The data processing phase takes less time, as pre-trained models do not need to be trained from scratch.
3. Data preparation technologies.
ETL Processes: Data extraction, transformation and loading tools such as Apache NiFi or Talend.
Data augmentation: e.g. inverting photos, changing contrast or modifying sounds.
Synthetic data creation: Generative techniques such as GAN (Generative Adversarial Networks) are used to create additional data.
4. Quality assurance.
Regardless of the method, data quality control is essential:
Balancing of uneven sets: ensuring that all data classes are equally represented.
Automated quality control: algorithms are used to identify errors in the data.
Feedback from the model: After the first model training, analysis allows you to identify weak points in the data sets and fix them.
AI-Engineering ensures that data is collected and prepared using the most advanced technologies and methodologies. This allows you to create reliable, efficient and accurate AI solutions that help solve real customer problems. Each stage of data processing is customized to the specifics of the project, ensuring the long-term value and success of AI systems.

DEVELOPMENT AND OPTIMIZATION OF ALGORITHMS
AI engineering also involves the design and development of algorithms that analyze data and make decisions. Various models are developed, such as machine learning models, neural networks, and deep learning models, depending on the requirements of the task. Models are refined using methodologies such as hyperparameter tuning, learning rate control, and other techniques to maximize their efficiency and accuracy. Automated training processes, such as CI/CD (continuous integration and continuous delivery) pipelines, allow for more efficient and faster model development and deployment. Choosing the right algorithm is vital to the success of any AI system. Engineers evaluate a problem (such as classification or regression) to determine the most appropriate machine learning algorithm, including deep learning paradigms. Once an algorithm is selected, it is necessary to optimize it through hyperparameter tuning to increase efficiency and accuracy. Techniques such as grid search or Bayesian optimization are applied, and engineers often use parallelization to speed up training processes, especially for large models and data sets.

DEEP LEARNING AND NATURAL LANGUAGE PROCESSING
Deep learning is particularly important for tasks involving large and complex data sets. Engineers design neural network architectures tailored to specific applications, such as convolutional neural networks for visual tasks or recurrent neural networks for sequence-based tasks. Transfer learning, where pre-trained models are adapted to specific use cases, helps simplify development and often improves performance.
Optimization for deployment in resource-constrained environments, such as mobile devices, includes techniques such as downsampling and quantization to reduce model size while maintaining performance. Engineers also reduce data heterogeneity through augmentation and artificial data generation, ensuring reliable model performance across classes. Natural language processing (NLP) is an important part of AI engineering that focuses on the ability of machines to understand and generate human language. The process begins with text preparation to prepare the data for machine learning models. Recent advances, especially transformer-based models such as BERT and GPT, have greatly improved the ability to understand the context of language. AI engineers work on a variety of NLP tasks, including sentiment analysis, machine translation, and information retrieval. These tasks require complex models that use attention mechanisms to increase accuracy. Applications include virtual assistants and chatbots, as well as specialized tasks such as named object recognition (NER) and part-of-speech labeling (POS). It all starts with data preparation. This means cleaning, annotating, and transforming the data so that it can be used in machine learning models. Such preparation includes word normalization, missing data handling, and syntactic and semantic analysis. Recent innovations, especially models based on transformer architectures such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), have significantly improved the understanding of speech context. These models train computers to understand the complex nuances of language and generate text that naturally reflects the way humans speak.
AI engineers use NLP to solve a variety of tasks, such as sentiment analysis - a technique used to determine the mood or sentiment of text, which is particularly valuable in business analytics and social media monitoring.
Machine translation: The automatic translation of text from one language to another, using deep learning algorithms to ensure accuracy and contextuality. Information extraction: This involves recognizing and extracting relevant information such as names, dates, locations, and other specifics from unstructured text. NLP has also been extended to multimodal solutions, where text is combined with video and audio data.
Image analysis: Integration with NLP can help analyze image content by recognizing objects and their contextual properties, which are then described in words.
Audio processing: NLP techniques, such as automatic speech recognition, allow the translation of spoken language into text, which is then analyzed, interpreted, or even translated. NLP applications are widely used to create virtual assistants and chatbots that can perform complex tasks such as customer service, reservation management, or technical support. Specialized tasks, such as named object recognition (NER) and part-of-speech tagging (POS), help improve text analysis by allowing detailed understanding and structuring of texts.
These NLP solutions are an integral part of modern AI systems, enabling the efficient and targeted use of artificial intelligence in various industries such as healthcare, finance, and public administration.

DECISION MAKING APPLICATIONS
AI-Engineering actively develops decision-making systems that use artificial intelligence technologies to automatically analyze data and make decisions in real time. These solutions are used in various fields, from autonomous transportation to financial management and industrial automation.symbolic AI (Artificial Intelligence): in autonomous vehicles:
The process of developing systems.
Projects usually begin with a detailed needs analysis and the creation of a system specification. This includes: data analysis and modeling to determine what data is needed for decision-making and how it should be processed; architectural design, which includes the creation of both a data processing and decision-making model.; prototyping, which allows for rapid testing and improvement of various decision-making strategies.
Decision-making technologies.
The most commonly used technologies in decision-making systems:
Symbolic AI (Artificial Intelligence): uses logical operations and predefined rules to model complex decision-making processes. This is effective in areas where rules and procedures can be clearly defined, such as in automated legal decision-making analysis.
Probabilistic models such as Bayesian networks: These models are used to manage uncertainty, where decisions are made based on probabilistic inferences about events. They are particularly useful in financial market analysis and risk management.
Deep learning technologies: These technologies are used to create complex decision-making systems, such as autonomous vehicle navigation systems. Neural networks can learn from huge data sets and make decisions based on constantly changing environmental conditions.
Practical applications.
Solutions are applicable in a variety of environments.
In autonomous vehicles: systems that analyze traffic conditions in real time, solve route optimization tasks, and ensure safe vehicle control.
In industrial automation: decision-making systems that manage production processes, optimize resource utilization, and minimize production disruptions.
In the financial sector: systems for risk assessment, portfolio management and market trend analysis, helping financial institutions make informed decisions.
Challenges and trends.
While AI-Engineering offers great potential for decision-making automation, it faces challenges related to data security, over-reliance on the system and ethical issues, so it is necessary to constantly improve the technology, ensuring that decisions are made responsibly and transparently. These solutions are undoubtedly changing the way companies operate, providing them with tools that help them manage complex tasks more effectively and adapt to rapidly changing market conditions.

INTEGRATION OF THE SYSTEM
The integration of artificial intelligence (AI) systems is a crucial step to ensure that a trained model is effectively implemented and works in a real environment. At this stage, the model becomes an integral part of the broader infrastructure, interacting with software components, databases, and user interfaces.
1. Connecting the AI ​​model to the system
The integration process involves several key steps. Communication with other systems: The AI ​​model must be connected to existing databases, APIs, cloud, or on-premises systems. This allows it to receive data in real time and return results in appropriate formats.
User interface customization: The model must be integrated into user interfaces so that end users can take advantage of the AI ​​features without additional technical training.
Interpretation of results: The model’s output can be presented in charts, reports, or other visualization formats that help in decision-making.
2. Deploying models from scratch and using pre-trained models
Models from scratch: often require more work, as the architecture needs to be designed to meet specific performance requirements. Often tailored to specialized environments, such as edge computing or specific hardware infrastructure. Additional optimization may be required for system compatibility.
Pre-trained models: typically easier to integrate, as they are built using standardized technologies that are compatible with most modern infrastructures. Requires less customization, especially when using models offered by cloud providers, such as Google Cloud AI, AWS SageMaker, or Microsoft Azure AI.
3. Containerization and automated deployment
To ensure a smooth integration process, engineers use containerization technologies such as Docker and Kubernetes. These technologies allow:
Create a consistent environment: The model, along with all its dependencies, is packaged in a container, which ensures that it will run independently of the underlying system.
Facilitate scaling: With Kubernetes, models can be easily adapted to high loads, for example, when the number of users increases.
Automate deployment: CI/CD (continuous integration and continuous delivery) processes allow for quick and efficient model updates and deployment.
4. Scaling and optimization
During integration, it is necessary to ensure that the system:
Operates efficiently at large scales: this is especially important for solutions that work with real-time data, such as streaming video analytics or large-scale data processing.
Supports efficient use of resources: Model performance optimization ensures that only as many resources as necessary are used, for example, by reducing the load on the processor or graphics processor.
5. Testing and control
Each integrated model is thoroughly tested in order to: verify functionality and ensure that the model properly performs the tasks assigned to it; measure performance, which evaluates the response time, accuracy, and reliability of the model in a real-world environment; ensure security: models are protected from potential security threats, including unauthorized access or data leakage.
6. AI-Engineering approach
AI-Engineering provides integration services that ensure seamless interaction between AI models and existing systems, flexible deployment on cloud platforms or on-premises infrastructures, and ongoing maintenance to keep models performing and adapting to changing requirements.
This process ensures that clients receive a fully functional and reliable AI solution that can be easily integrated into their work processes and help achieve strategic goals.

TESTING & VALIDATION
Testing and validation are essential steps in developing artificial intelligence (AI) systems. These processes ensure that models perform as intended, are reliable, accurate, and safe for use in real-world environments. The methods and depth of testing can vary depending on whether the model is being built from scratch or using a pre-trained model.
1. Testing models built from scratch
For models built from scratch, the testing process is comprehensive and includes:
Functional testing:
Verifying that each part of the model works according to its intended functionality.
Ensuring that the model components (e.g., neural network layers or hyperparameters) work together smoothly.
Stress testing:
The model is tested under high loads to determine how it performs under complex operating conditions.
Response time and stability are analyzed when working with large amounts of data or fast decision-making requirements.
Stress testing:
The model is tested to see if it properly handles atypical situations or unusual data, such as unusual structure or extreme values.
2. Testing pre-trained models
When using pre-trained models, the testing process is focused on assessing the quality of model fine-tuning:
Contextual accuracy:
It is checked whether the model adapts properly to a new task and environment. Functional tests confirm that the model output meets the specific requirements of the task.
Data compatibility:
It is ensured that the model effectively uses task-specific data and provides accurate results in its context.
3. Bias and Fairness Assessments
Bias and fairness tests are essential, especially in areas where AI decisions have a direct impact on people (e.g., Bias Identification:
Analyzes whether the model’s outputs are biased by gender, race, age, or other factors. Corrects situations where signs of bias are observed.
Fairness Assessment:
Verifies whether the model’s decisions are equally accurate and reliable for all data sets.
4. Security Review
Security tests ensure that the model and system are protected from potential threats:
Protection against adversarial attacks:
Verifies whether the model can withstand “adversarial attacks,” where data is specifically manipulated to deceive the model.
Data Security:
Ensures that the data used is protected from unauthorized access or leakage, in compliance with regulations such as the GDPR.
5. Ensuring Transparency
It is particularly important that AI decision-making processes are transparent.
Interpretation of predictions: Models should provide not only results but also explanations of how they were achieved, so that non-technical users can understand them. Technologies such as SHAP or LIME are used to interpret predictions.
Regulatory compliance: Ensures that the AI ​​system complies with applicable regulatory standards and ethical guidelines.
AI-Engineering uses standardized testing processes that include a detailed analysis of the model’s functionality, compatibility testing with specific industry areas, and feedback from users to further improve the solutions. Our goal is to ensure that each AI system is not only technologically advanced, but also ethical, reliable, and meets customer expectations. Testing and validation are key steps in achieving this.

IMPLEMENTATION AND MONITORING
The implementation of Artificial Intelligence (AI) solutions is one of the most important stages that ensures that the model is successfully integrated into the production environment and starts working as intended. This stage includes not only technical implementation, but also systematic performance monitoring, ensuring the long-term effectiveness of the model.
Models built from scratch
Implementing models built from scratch requires more attention and technical knowledge, as they may require:
Performance optimization: optimizing memory usage to reduce redundancy and improve response time, reducing latency so that the model responds quickly to real-time requests, adapting the model to work in resource-constrained environments, such as edge computing.
Specific adaptations: adjusting the model to a specific hardware or software context, adapting algorithms and infrastructure components, such as using GPUs for intensive calculations.
Pre-trained models
When deploying pre-trained models, the workload is reduced because the models are often optimized for production environments, such as cloud platforms such as AWS SageMaker or Google Cloud AI. The focus is on compatibility with task-specific data and integration into existing infrastructure, such as data pipelines or APIs. To reduce risk and ensure a smooth transition to a live environment, the following deployment techniques are often used:
Stage release: The model is released gradually to small groups of users, and expanded after successful performance.
A/B testing: Two variants of the model are compared to determine the more effective solution.
Canary release: The new model is deployed to only a subset of users, observing its performance before full deployment.
Deployment is just the beginning - ongoing monitoring and maintenance are essential for the long-term effectiveness of an AI solution.
Performance Monitoring
Model drift: When the accuracy of a model decreases due to changing data patterns, such as new user behavior or market changes, monitoring tools such as MLFlow or Prometheus are used to capture the model’s performance in real time.
Periodic fine-tuning and updates: For pre-trained models, periodic fine-tuning may be sufficient to adapt the model to new data or tasks. Models built from scratch often require more resource-intensive training processes:
Data updates and expansion.
Retraining the model.
Automated monitoring: Automated infrastructure allows for the detection of problems such as loss of accuracy, excessive use of memory or CPU resources, automatic alerts: notifications about potential problems are sent to the engineering team so that they can be resolved as quickly as possible.
AI-Engineering offers a complete solution that includes professional implementation using advanced technologies and best practices, continuous monitoring with detailed performance metrics, and rapid response to changes, ensuring that your AI solution remains effective and meets the latest requirements.
Our goal is not only to successfully implement an AI solution, but also to ensure that it is reliable, efficient, and continuously improved, adapting to changing needs.

REGULAR CARE
Regular maintenance includes checking model integrity and bias maintenance and security updates to protect against various attacks. Machine Learning Operations (MLOps) or Artificial Intelligence Operations (AIOps) is an important part of modern AI engineering. Similar to DevOps practices in software, MLOps provides a framework for continuous integration, continuous delivery (CI/CD), and automated monitoring of machine learning models throughout their lifecycle. This practice is popular among data scientists, AI engineers, and IT operations professionals, and ensures that AI models are deployed, monitored, and effectively maintained in real-world environments.
MLOps is especially important as AI systems scale to handle more complex tasks and larger data sets. Without robust MLOps practices, models risk underperforming or failing after deployment, causing issues such as downtime, ethical issues, or loss of stakeholder trust. By establishing automated, scalable processes, MLOps enables AI engineers to more effectively manage the entire lifecycle of machine learning models from development to deployment and ongoing monitoring. Additionally, regulation of AI systems continues to evolve, and MLOps practices are critical to ensuring compliance with legal requirements, including data privacy regulations and AI ethical guidelines. By incorporating MLOps best practices, organizations can reduce risk, maintain high performance, and responsibly deploy AI solutions.

SECURITY
Security is critical in AI engineering, especially as AI systems are increasingly integrated into sensitive and safety-critical applications. AI engineers implement robust security measures to protect models from adversarial attacks, such as spoofing and contamination, that can compromise the integrity and performance of the system. For example, adversarial training is used when models are exposed to malicious data, and training helps train and prepare systems to withstand cyberattacks. Protecting the data used for training is critical. Encryption, secure data storage, and access control mechanisms are implemented to protect sensitive information from unwanted access and intrusions. AI systems also require ongoing monitoring to identify and mitigate emerging vulnerabilities after deployment. In high-end environments, such as autonomous systems and healthcare, engineers incorporate failover and failover mechanisms to ensure that AI models continue to operate uninterrupted, even in the face of security breaches.

ETHICS & COMPLIANCE
As AI systems increasingly influence societal aspects, ethics and compliance are vital parts of AI engineering. Engineers develop models to mitigate risks, such as data contamination, and to ensure that AI systems comply with legal requirements, such as data protection regulations such as the GDPR. Privacy-preserving techniques, including data anonymization and differential privacy, are applied to protect personal information and ensure compliance with international standards.
Ethical considerations focus on minimizing the pre-determination of AI systems to prevent discrimination based on race, gender, or other protected characteristics. By designing fair and responsible AI solutions, engineers contribute to the creation of technologies that are not only technically sound but also socially responsible. As the impact of AI systems on society increases, ethics and compliance become increasingly important.
Data privacy and security: implementing strategies to protect personal data and ensure compliance with regulations (GDPR).
Ensuring fairness: reducing algorithmic bias, conducting extensive testing and analysis to ensure that decisions are not discriminatory.
Accountability and transparency: creating clear and understandable models to ensure that decision-making processes are reviewed and understood by stakeholders.
The combination of these aspects in AI engineering not only promotes technological progress, but also ensures that this progress is responsible and meets societal expectations. This makes AI engineering an integral part of modern technological ecosystems, guaranteeing the synergy of technology and ethics.

WORKLOAD
The work of an AI engineer is related to the implementation or life cycle of an AI system, which is a complex and multi-stage process. This process can involve building models from scratch or using pre-trained models, which usually depends on the project requirements. Each project has unique challenges and this affects the implementation time, resources and technical resources, which must also be assessed by the software engineer.

CHALLENGES
AI engineering faces a unique set of challenges that are different from traditional software development. One of the main issues is model drift, where AI models lose performance over time due to changes in data models, requiring constant retraining and adaptation. In addition, data privacy and security are critical issues, especially when sensitive data is used in cloud models. Ensuring model clarity is another challenge, as complex AI systems need to be made understandable to non-technical stakeholders. Bias and fairness also require close attention to prevent discrimination and provide neutral and fair results, as biases inherent in training data can propagate through AI algorithms, influencing results in unpredictable ways. Addressing these challenges requires an interdisciplinary approach, combining technical expertise with ethical and regulatory considerations.

SUSTAINABILITY
Training large AI models involves processing large data sets over long periods of time, consuming significant amounts of energy. This has raised concerns about the environmental impact of AI technologies, given the expansion of data centers required to support AI training and inference. The increasing demand for computing power has led to significant electricity consumption, and AI-based applications often leave a significant carbon footprint. In response to these impacts, AI engineers and researchers are looking for ways to mitigate these effects by developing energy-efficient algorithms, using green data centers, and relying on renewable energy sources. Ensuring the sustainability of AI systems is becoming a critical aspect of responsible AI development as the industry continues to grow globally.

EDUCATION
AI engineering education typically includes advanced courses in software and data engineering. Key topics include machine learning, deep learning, natural language processing, and computer vision. Many universities now offer specialized AI engineering programs at both undergraduate and graduate levels, including hands-on lab work, project-based learning, and interdisciplinary courses that combine AI theory with engineering practices. Professional qualifications can also complement formal education. In addition, hands-on experience with real-world projects, internships, and contributions to open-source AI initiatives are highly recommended for gaining hands-on experience.