Introduction to Azure AI Foundry Artificial Intelligence (AI) is transforming the way organizations develop software, automate business processes, analyze information, and interact with customers. Today, AI-powered applications are used in customer support, healthcare, education, finance, software development, and many other industries. However, building an AI application requires more than simply selecting an AI model. Developers …

Azure AI Foundry Explained: A Complete Guide to Building, Deploying, and Managing AI Applications Read More »

Cloud AI & Cloud-Native Machine Learning | AI Services | Dezlearn Education Artificial Intelligence (AI) is transforming how businesses develop software, automate processes, analyze information, and interact with customers. One of the most significant developments in recent years has been the rise of Generative AI, which allows machines to generate human-like text, write code, summarize …

Azure OpenAI Service: A Complete Guide to Building Generative AI Applications on Microsoft Azure Read More »

Artificial Intelligence is no longer limited to research laboratories or organizations with large teams of data scientists. Today, cloud platforms provide ready-to-use AI capabilities that developers can integrate directly into applications. Microsoft Azure is one of the major cloud platforms offering these capabilities. With Azure’s AI ecosystem, developers can add features such as: Speech recognition …

Cloud AI & Cloud-Native Machine Learning: Introduction to Azure AI Services Read More »

Artificial Intelligence has changed the way we think about computing power. Traditional applications typically rely heavily on CPUs. However, modern AI applications often need to perform enormous numbers of mathematical calculations simultaneously. Training a deep learning model may involve processing millions or billions of parameters across huge datasets. This is where specialized computing hardware becomes …

Cloud Fundamentals: GPUs vs TPUs Explained Read More »

Cloud computing has become the foundation of modern software development. From websites and mobile applications to data platforms, machine learning systems, and Generative AI applications, most modern technology relies on cloud infrastructure in some form. When people first start learning cloud computing, platforms such as AWS, Microsoft Azure, and Google Cloud can appear complicated because …

Cloud Fundamentals: Understanding Compute, Storage, and Networking Read More »

Artificial Intelligence applications may look simple from the outside. A user enters a prompt, uploads an image, asks a question, or submits some data—and within seconds, the application returns an intelligent response. For example: User → AI Application → AI Model → Response But behind this simple interaction is often a sophisticated cloud architecture involving …

Cloud Fundamentals: Cloud Architecture for AI Applications Read More »

Cloud computing has become one of the foundations of modern software development, data engineering, DevOps, and artificial intelligence. When organizations build a new application today, they often do not purchase physical servers, install them in a data center, and maintain everything themselves. Instead, they rent computing resources from cloud providers. Three platforms dominate many enterprise …

Cloud Fundamentals: Choosing Between AWS, Azure, and Google Cloud Read More »

Artificial Intelligence is now used in chatbots, recommendation systems, fraud detection, image recognition, coding assistants, automation tools, and Large Language Models (LLMs). But when we use an AI application, an important question is often overlooked: Where does the AI actually run? Some AI models can run on laptops, mobile devices, or local servers. However, many …

Cloud Fundamentals: Why AI Runs in the Cloud Read More »

Machine learning models are built to learn patterns from historical data and use those patterns to make predictions on new, unseen data. But sometimes a model performs exceptionally well during training and evaluation—and then performs poorly after deployment. One of the most common reasons for this unexpected behavior is data leakage. Data leakage occurs when …

Data Leakage and How to Avoid It Read More »

Handling Imbalanced Datasets using SMOTE Machine Learning models are only as good as the data they learn from. One of the most common challenges faced by data scientists is imbalanced datasets, where one class significantly outnumbers the other(s). This issue is especially common in fraud detection, medical diagnosis, anomaly detection, manufacturing defect detection, cybersecurity, and …

Machine Learning Fundamentals & Advanced ML Read More »