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Introduction to AI Microservices The rapid evolution of artificial intelligence has driven organizations to seek scalable, flexible, and maintainable ways to deploy machine learning (ML) solutions. One of the most effective architectural paradigms to address these needs is the microservices approach. In this section, we explore what microservices are, why they are particularly beneficial for […]

Introduction to Custom Deep Learning Architectures The rapid evolution of deep learning has led to the development of a wide range of neural network architectures, many of which are available as ready-to-use models in popular frameworks. However, there are numerous scenarios where off-the-shelf solutions are insufficient, and designing a custom architecture becomes necessary. This introduction

Introduction to Model Optimization and Quantization Optimization and quantization of artificial intelligence models are key topics that enable increased computational efficiency and reduced hardware resource requirements. In the era of growing AI popularity, both in industrial and consumer applications, the ability to run advanced models on devices with limited computational power—such as smartphones, IoT devices,

Introduction to Federated Learning In the era of big data and artificial intelligence, the demand for privacy-preserving machine learning solutions is stronger than ever. Traditional AI models are typically trained on centralized datasets, which often requires collecting sensitive user data in one location. This approach raises significant privacy concerns, especially in sectors like healthcare, finance,

 Introduction In recent years, Convolutional Neural Networks (CNNs) and Transformers have revolutionized the field of artificial intelligence, powering breakthroughs in computer vision, natural language processing, and beyond. However, as AI applications grow more complex and diverse, these traditional architectures sometimes fall short in addressing new challenges. This article explores why CNNs and Transformers may no

Introduction: The Importance of Hyperparameter Optimization Hyperparameter optimization is one of the key stages in building effective artificial intelligence models. Proper selection of these parameters can significantly improve model performance, while suboptimal settings often lead to poor results, overfitting, or underfitting. What are Hyperparameters and Why Are They Important? Hyperparameters are external settings of a

Introduction: What is Machine Learning and What is Deep Learning? Modern artificial intelligence (AI) is based on two key approaches: Machine Learning (ML) and Deep Learning (DL). Although these terms are often used interchangeably, in reality they refer to different technologies and methods of data analysis. Machine Learning is a field of computer science focused

Introduction to AI Model Optimization Why is AI Model Optimization Important? AI model optimization is a crucial step in the machine learning lifecycle, directly impacting the efficiency, accuracy, and scalability of AI solutions. As organizations increasingly rely on AI to automate processes, make predictions, and drive business value, the need to optimize models becomes more