What is a Web Server and an Application Server? 7 Main Benefits of an App Server

Docker is software for containerizing applications. Today, we’ll talk about what containerization and Docker are, what they are used for, and what advantages they bring. Containerization Containerization is one of the methods of virtualization. To understand it better, let’s take a brief historical detour. In the 1960s, computers couldn’t perform multiple tasks at once. This led to long queues for access to such rare machines. The solution was to distribute computing power among different isolated processes. That’s how the history of virtualization began. Virtualization is the allocation of computing resources to isolated processes within a single physical device. The main development of virtualization came during the Internet era. Imagine you’re a business owner and you want your company to have a website. You need a server connected to the global network. Today, that’s as easy as visiting hostman.com and choosing a server that fits your needs. But in the early days of the internet, such convenient services didn’t exist. Companies had to buy and maintain servers on their own, which was inconvenient and expensive.  This problem led to the rise of hosting providers: companies that purchased hardware, placed it in their facilities, and rented out servers. As technology advanced, computers became more powerful, and dedicating a full physical server to a single website became wasteful. Virtualization helped: several isolated virtual machines could run on one computer, each hosting different websites. The technology allowed allocating exactly as many resources as each site needed. However, that still wasn’t enough. As the internet evolved, the number of applications required for running a website grew, and each required its own dependencies. Eventually, it became “crowded” within a single virtual machine. One workaround was to host each application in its own virtual machine, a kind of virtual “matryoshka doll.” But a full VM was still excessive for a single application: it didn’t need a full OS instance. Meanwhile, virtual machines consumed a lot of resources, much of which went unused. The solution was containerization. Instead of running a separate virtual machine for each application, developers found a way to run them in isolation within the same operating system. Each container includes the application, its dependencies, and libraries: an isolated environment that ensures consistent operation across systems. Docker What is a program? It’s a piece of code that must be executed by the CPU. When you run a container, Docker (through the containerd component) creates an isolated process with its own namespace and file system. To the host system, the container looks like a regular process, while to the program inside it, everything appears as if it’s running on its own dedicated system. Containers are isolated but can communicate with each other via networks, shared volumes, or sockets, if allowed by configuration. Data Storage Isolation from the host OS raises a natural question: how to store data? Docker Volume: a storage unit created and managed by Docker itself. It can be located anywhere: within the host’s file system or on an external server. Bind Mount: storage manually created by the user on the host machine, which is then mounted into containers during runtime. tmpfs Volume: temporary in-memory storage. It is erased when the container stops. In production environments, volumes are most commonly used, as Docker manages them more securely and reliably. Docker Architecture Docker’s architecture consists of several key components that work together to build, run, and manage containers: Docker Host A physical or virtual machine running the Docker Engine. This is where containers and images are executed. Docker Engine (Docker Daemon) The central service responsible for building, running, and managing containers. Since Docker 1.11, Docker Engine has used containerd, a low-level component that directly manages container lifecycles (creation, start, stop, and deletion). containerd A container runtime that interacts with the operating system kernel to execute containers. It’s used not only by Docker but also by other systems such as Kubernetes. Docker Engine communicates with containerd via an API, passing commands received from the client. Docker CLI (Client) The command-line interface through which users interact with Docker. CLI commands are sent to the Docker Daemon via REST API (usually over a Unix socket or TCP). Docker Image A Docker image is a template that includes an application and all its dependencies. It’s similar to a system snapshot from which containers are created. Dockerfile A text file containing instructions on how to build an image. It defines the base image, dependency installation commands, environment variables, and the application’s entry point. Docker Container A Docker container is a running instance of an image. A container is isolated from other processes and uses host resources through Docker Engine and containerd. Docker Registry A repository for storing and distributing Docker images. There are public and private registries. The most popular public one is Docker Hub, which Docker connects to by default. Docker Compose A tool for defining and running multi-container applications using YAML files. It allows developers to configure service dependencies, networks, and volumes for entire projects. Advantages of Docker Security What does isolation provide in terms of security? An isolated application cannot harm the host operating system. It has no access to the host’s file system, preventing data leaks. Any application-related crash won’t affect the host OS. Compatibility A container image can be run on any device with Docker installed. Automation Docker automates application deployment and configuration, saving time and reducing human error. Shared Repositories Docker users have access to repositories with thousands of ready-to-use images for various purposes. Resource Efficiency Unlike virtual machines, Docker containers don’t require a separate OS instance, allowing better use of computational resources. Using Docker Now let’s move from theory to practice. The first thing we need to do is install Docker. Installation Installation begins at the official website: docker.com. Go to the “Get Started” section and choose the version for your operating system. In our case, it’s Windows. Installation guides for other OSs are also available. After installation, a system reboot is required. Docker requires a hypervisor, special software that enables multiple operating systems to run simultaneously. We’ll use WSL2 (Windows Subsystem for Linux 2). Docker installs WSL2 automatically, but you must manually download the latest Linux kernel update. Go to Microsoft’s website, download, and install the update package. After rebooting, Docker Desktop will open. Running a Python Script Let’s print the message “Hello, World” to the console using a simple Python script: #!/usr/bin/python3 print("Hello World") Since we’re not running the script directly, we need a shebang—that’s the first line in the script. In short, the shebang tells the Linux kernel how to execute the script. Let’s name our file the classic way: main.py. Now open the command line. To run the script, execute: docker run -v D:\script_dir:/dir python:3 /dir/main.py Let’s break this down: docker run runs a container -v mounts a directory (bind mount) D:\script_dir is the directory with our script /dir is the mount point inside the container python:3 is the image /dir/main.py is the executable file (our script) What happens when this command is executed? Docker searches for the python:3 image first locally, then in the registry, and deploys it. Next, it mounts our script directory into the container and runs the script inside it. Conclusion In this article, we explored what Docker is, how it works, and even ran our first script. Docker and containerization are not a cure-all, but they’re invaluable tools in modern software development.

“New electricity”—that’s what many people call artificial intelligence today. Some see AI as another tech bubble, while others believe our lives will become unrecognizable within five to seven years. We’re already seeing AI become part of everyday life, often without realizing it. For example, every modern search engine not only shows relevant links but also tries to directly answer your question. The growing popularity of AI is closely tied to the rise of chat interfaces, which gradually came to be known as AI assistants. In this article, we’ll take a detailed look at the best AI assistants, explore their key features, and see how these technologies are changing our lives. The Evolution of AI: From Narrow Assistants to Autonomous Agents At first glance, all AI assistants might seem similar, but they can actually be divided into several categories based on their level of autonomy. An AI assistant is primarily a reactive system that performs direct user commands. It handles simple, repetitive tasks such as checking the weather or setting an alarm. Classic examples include Siri, Google Assistant, and Alexa. An AI agent, on the other hand, is an autonomous system capable of acting independently, requiring little to no human intervention. Agents can manage complex processes such as optimizing information retrieval, generating reports, or automatically blocking suspicious financial activity. Often, a “swarm” of agents is created—each performs its own task and passes the result to the next. The line between these concepts is gradually blurring. Modern AI assistants equipped with self-learning and adaptive capabilities occupy an intermediate stage of evolution. By the end of 2025, almost every enterprise application will include a built-in assistant. By 2026, assistants are expected to evolve into highly specialized agents capable of autonomous operation, and by 2028, up to 15% of daily business decisions will be made by autonomous AI agents. The Best AI Assistants: An Overview of Key Players To choose the right AI assistant, it’s important to review the leading solutions on the market. Google Gemini. Probably the best AI assistant for those deeply integrated into the Google Workspace ecosystem. Its integration with Google Docs and Google Drive enables Gemini to provide precise, context-aware responses based on real user or company data. One of Gemini’s most interesting features is Gems: personalized expert profiles that users can create for specific domains and access on demand. This eliminates the need to repeatedly type detailed instructions in chat. ChatGPT. One of the first and most influential products, developed by OpenAI. It popularized the concept of chatting with large language models in a conversational window. With a wide range of integrations, the ability to create custom experts, and even voice interaction, ChatGPT is now used by more than 800 million people every week. Microsoft Copilot. Designed for seamless integration with Microsoft tools such as Microsoft 365, Excel, and Teams. If your organization relies on Microsoft’s ecosystem, Copilot becomes an indispensable productivity partner. Anthropic Claude. Claude is a large language model widely used in AI agent development. Beyond that, it’s known for its exceptional text generation capabilities. Claude’s writing style is diverse and natural, producing content that “sounds human,” while models like GPT or Gemini often repeat the same buzzwords such as “seamless” or “robust.” Specialized AI Assistants Specialized AI assistants are designed for specific tasks within narrow domains, unlike general-purpose models. They’re trained on company- or industry-specific datasets, ensuring high precision and relevance in fields like law or medicine. This focused approach increases performance and speed, as such models use optimized architectures. Their modular design also makes them easily adaptable to new technologies, providing cost efficiency and longevity. As a result, specialized AIs are becoming key components of business process automation, complementing general-purpose assistants. Industry Applications Specialized AI assistants are already being used across industries, solving concrete, high-value problems. Law. AI assistants such as Legal Robot and Harvey analyze legal documents, search for relevant laws, and even predict case outcomes. Healthcare. Systems trained on medical data assist in diagnostics, image analysis, and treatment protocol development (for example, Qure.AI). They’re also embedded into wearable devices such as Apple Watch and Oura smart rings for health monitoring. Finance. Models like GiaGPT and Salesforce Einstein detect fraud, assess credit risks, and automate accounting operations. Software Development. Assistants, including Cursor and Replit, help developers write, debug, and test code, cutting development time by up to 50%. Marketing. Tools like Writesonic and TurboText automate content creation, analyze customer behavior, and personalize offers. How AI Is Changing Our Lives: From Productivity to Cognitive Risks The adoption of AI assistants has a profound impact on many aspects of human life. Transformation of the labor market and productivity growth. AI assistants can save up to 35% of employees’ working time by automating routine operations. A PwC report shows that industries adopting AI experience revenue growth three times faster than those that don’t. Employees with AI-related skills, such as prompt engineering, earn on average 56% more. The era of the “single answer” (AEO). With the rise of chatbots, traditional SEO (Search Engine Optimization) is giving way to AEO, Answer Engine Optimization. In this new reality, the goal is no longer “to rank high,” but “to become the answer.” This creates a high barrier to entry: content not selected by AI as the definitive answer becomes invisible to a large share of users. Cognitive debt and digital amnesia. Excessive dependence on devices can weaken memory, reduce focus, and impair learning ability. Research shows that while AI use increases efficiency, it can also lower cognitive performance, as the brain activates fewer neural connections. This phenomenon, known as “cognitive debt,” describes how reduced mental engagement in the moment decreases our ability to form new skills later. Impact on social relationships. AI companions can help reduce loneliness, but they also risk deepening social isolation. They can become a kind of “crutch” that replaces—but doesn’t truly substitute—complex human interactions. Choosing Your Personal AI Assistant There’s no one-size-fits-all AI assistant. The best choice depends on your goals and work environment. General-purpose models such as Google Gemini and ChatGPT handle a wide range of requests effectively, but for maximum efficiency, they’re often combined with specialized AI agents. Thanks to RAG (Retrieval-Augmented Generation) technology, narrow-domain AI agents can act as true experts, automating thousands of specific tasks, analyzing data, and providing highly accurate answers. The future of AI assistants isn’t just about technological advancement. It’s about deep integration into business processes. The right combination of general-purpose and specialized tools will unlock unprecedented gains in productivity.

Machine learning and artificial intelligence in 2025 continue to transform business processes, from logistics automation to personalization of customer services. However, regular processors (CPUs) are no longer sufficient for effective work with neural networks. Graphics cards for AI (GPUs) have become a key tool for accelerating model training, whether it's computer vision, natural language processing, or generative AI. Why GPUs Are Essential for ML and AI Graphics cards for AI are not just computing devices, but a strategic asset for business. They allow reducing the development time of AI solutions, minimizing costs, and bringing products to market faster. In 2025, neural networks are applied everywhere: from demand forecasting in retail to medical diagnostics. GPUs provide parallel computing necessary for processing huge volumes of data. This is especially important for companies where time and accuracy of forecasts directly affect profit. Why CPU Cannot Handle ML Tasks Processors (CPUs) are optimized for sequential computing. Their architecture with 4-32 cores is suitable for tasks like text processing or database management. However, machine learning requires performing millions of parallel operations, such as matrix multiplication or gradient descent. CPUs cannot keep up with such loads, making them ineffective for modern neural networks. Example: training a computer vision model for defect recognition in production. With CPU, the process can take weeks, and errors due to insufficient power lead to downtime. For business, this means production delays and financial losses. Additionally, CPUs do not support optimizations such as low-precision computing (FP16), which accelerate ML without loss of quality. The Role of GPU in Accelerating Model Training GPUs with thousands of cores (from 2,000 to 16,000+) are designed for parallel computing. They process tensor operations that form the basis of neural networks, tens of times faster than CPUs. In 2025, this is especially noticeable when working with large language models (LLMs), generative networks, and computer vision systems. Key GPU Specifications for ML Let’s talk about factors to consider when selecting GPUs for AI.  Choosing a graphics card for machine learning requires analysis of technical parameters that affect performance and profitability. In 2025, the market offers many models, from budget to professional. For business, it's important to choose a GPU that will accelerate development and reduce operational costs. Characteristic Description Significance for ML VRAM Volume Memory for storing models and data Large models require 24-80 GB CUDA Cores / Tensor Cores Blocks for parallel computing Accelerate training, especially FP16 Framework Support Compatibility with PyTorch, TensorFlow, JAX Simplifies development Power Consumption Consumed power (W) Affects expenses and cooling Price/Performance Balance of cost and speed Optimizes budget Video Memory Volume (VRAM) VRAM determines how much data and model parameters can be stored on the GPU. For simple tasks such as image classification, 8-12 GB is sufficient. However, for large models, including LLMs or generative networks, 24-141 GB is required (like the Tesla H200). Lack of VRAM leads to out-of-memory errors, which can stop training. Case: A fintech startup uses Tesla A6000 with 48 GB VRAM for transaction analysis, accelerating processing by 40%. Recommendation: Beginners need 12-16 GB, but for corporate tasks choose 40+ GB. Number of CUDA Cores and FP16/FP32 Performance CUDA cores (for NVIDIA) or Stream Processors (for AMD) provide parallel computing. More cores mean higher speed. For example, Tesla H200 with approximately 14,592 cores outperforms RTX 3060 with approximately 3,584 cores. Tensor Cores accelerate low-precision operations (FP16/FP32), which is critical for modern models. Case: An automotive company trains autonomous driving models on Tesla H100, reducing test time by 50%. For business, this means development savings. Library and Framework Support (TensorFlow, PyTorch) A graphics card for AI must support popular frameworks: TensorFlow, PyTorch, JAX. NVIDIA leads thanks to CUDA, but AMD with ROCm is gradually catching up. Without compatibility, developers spend time on optimization, which slows down projects. Case: A marketing team uses PyTorch on Tesla A100 for A/B testing advertising campaigns, quickly adapting models to customer data. Power Consumption and Cooling Modern GPUs consume 200-700W, requiring powerful power supplies and cooling systems. In 2025, this is relevant for servers and data centers. Overheating can lead to failures, which is unacceptable for business. Case: A logistics company uses water cooling for a GPU cluster, ensuring stable operation of forecasting models. Price and Price-Performance Ratio The balance of price and performance is critical for return on investment (ROI) and long-term efficiency of business projects. For example, Tesla A6000, offering 48 GB VRAM and high performance for approximately $5,000, pays for itself within a year in projects with large models, such as financial data processing or training complex neural networks. However, choosing the optimal graphics card for neural networks depends not only on the initial cost, but also on operating expenses, including power consumption and the need for additional equipment, such as powerful power supplies and cooling systems. For small businesses or beginning developers, a graphics card for machine learning, such as RTX 3060 for $350-500, can be a reasonable start. It provides basic performance for educational tasks, but its limited 12 GB VRAM and approximately 3,584 CUDA cores won't handle large projects without significant time costs. On the other hand, for companies working with generative models or big data analysis, investing in Tesla H100 for $20,000 and more (depending on configuration) is justified by high training speed and scalability, which reduces overall costs in the long term. It's important to consider not only the price of the graphics card itself, but also additional factors, such as driver availability, compatibility with existing infrastructure, and maintenance costs. For example, for corporate solutions where high reliability is required, Tesla A6000 may be more profitable compared to cheaper alternatives, such as A5000 ($2,500-3,000), if we consider reduced risks of failures and the need for frequent equipment replacement. Thus, the price-performance ratio requires careful analysis in the context of specific business goals, including product time-to-market and potential benefits from accelerating ML processes. Best Graphics Cards for AI in 2025 The GPU market in 2025 offers the best solutions for different budgets and tasks. Optimal Solutions for Beginners (under $1,000) For students and small businesses, the best NVIDIA graphic card for AI would be RTX 4060 Ti (16 GB, approximately $500). This graphics card will handle educational tasks excellently, such as data classification or small neural networks. RTX 4060 Ti provides high performance with 16 GB VRAM and Tensor Cores support. Alternative: AMD RX 6800 (16 GB, approximately $500) with ROCm for more complex projects. Case: A student trains a text analysis model on RTX 4060 Ti. Mid-Range: Balance of Power and Price NVIDIA A5000 (24 GB, approximately $3,000) is a universal choice for medium models and research. It's suitable for tasks like data analysis or content generation. Alternative: AMD Radeon Pro W6800 (32 GB, approximately $2,500) is a powerful competitor with increased VRAM and improved ROCm support, ideal for medium projects. Case: A media company uses A5000 for generative networks, accelerating video production by 35%. Professional Graphics Cards for Advanced Tasks Tesla A6000 (48 GB, approximately $5,000), Tesla H100 (80 GB, approximately $30,000), and Tesla H200 (141 GB, approximately $35,000) are great for large models and corporate tasks. Alternative: AMD MI300X (64 GB, approximately $20,000) is suitable for supercomputers, but inferior in ecosystem. Case: An AI startup trains a multimodal model on Tesla H200, reducing development time by 60%. NVIDIA vs AMD for AI NVIDIA remains the leader in ML, but AMD is actively catching up. The choice depends on budget, tasks, and ecosystem. Here's a comparison: Parameter NVIDIA AMD Ecosystem CUDA, wide support ROCm, limited VRAM 12-141 GB 16-64 GB Price More expensive Cheaper Tensor Cores Yes No Community Large Developing Why NVIDIA is the Choice of Most Developers NVIDIA dominates thanks to a wide range of advantages that make it preferred for developers and businesses worldwide: CUDA: This platform has become the de facto standard for ML, providing perfect compatibility with frameworks such as PyTorch, TensorFlow, and JAX. Libraries optimized for CUDA allow accelerating development and reducing costs for code adaptation. Tensor Cores: Specialized blocks that accelerate low-precision operations (FP16/FP32) provide a significant advantage when training modern neural networks, especially in tasks requiring high performance, such as generative AI. Energy Efficiency: The new Hopper architecture demonstrates outstanding performance-to-power consumption ratio, which reduces operating costs for data centers and companies striving for sustainable development. Community Support: A huge ecosystem of developers, documentation, and ready-made solutions simplifies the implementation of NVIDIA GPUs in projects, reducing time for training and debugging. Case: A retail company uses Tesla A100 for demand forecasting, reducing costs by 25% and improving forecast accuracy thanks to broad tool support and platform stability. AMD GPU Capabilities in 2025 AMD offers an alternative that attracts attention thanks to competitive characteristics and affordable cost: ROCm: The platform is actively developing, providing improved support for PyTorch and TensorFlow. In 2025, ROCm becomes more stable, although it still lags behind CUDA in speed and universality. Price: AMD GPUs, such as MI300X (approximately $20,000), are the best budget GPUs for AI, as they are significantly cheaper than NVIDIA counterparts. It makes them attractive for universities, research centers, and companies with limited budgets. Energy Efficiency: New AMD architectures demonstrate improvements in energy consumption, making them competitive in the long term. HPC Support: AMD cards are successfully used in high-performance computing, such as climate modeling, which expands their application beyond traditional ML. Case: A university uses MI300X for research, saving 30% of budget and supporting complex simulations thanks to high memory density. However, the limited ROCm ecosystem and smaller developer community may slow adoption and require additional optimization efforts. Local GPU vs Cloud Solutions Parameter Local GPU Cloud Control Full Limited Initial Costs High Low Scalability Limited High When to Use Local Hardware Local GPUs are suitable for permanent tasks where autonomy and full control over equipment are important. For example, the R&D department of a large company can use Tesla A6000 for long-term research, paying for itself within a year thanks to stable performance. Local graphics cards are especially useful if the business plans intensive daily GPU use, as this eliminates additional rental costs and allows optimizing infrastructure for specific needs. Case: A game development company trains models on local A6000s, avoiding cloud dependency. Additionally, local solutions allow configuring cooling and power consumption for specific conditions, which is important for data centers and server rooms with limited resources. However, this requires significant initial investments and regular maintenance, which may not be justified for small projects or periodic tasks. Pros and Cons of Cloud Solutions Cloud solutions for GPU usage are becoming a popular choice thanks to their flexibility and accessibility, especially for businesses seeking to optimize machine learning costs. Let's examine the key advantages and limitations to consider when choosing this approach. Pros: Scalability: You can add GPUs as tasks grow, which is ideal for companies with variable workloads. This allows quick adaptation to new projects without needing to purchase new equipment. Flexibility: Paying only for actual usage reduces financial risks, especially for startups or companies testing new AI solutions. For example, you can rent Tesla A100 for experiments without spending $20,000 on purchase. Access to Top GPUs: Cloud providers give access to cutting-edge models that aren't available for purchase in small volumes or require complex installation. Updates and Support: Cloud providers regularly update equipment and drivers, relieving businesses of the need to independently monitor technical condition. Cons: Internet Dependency: Stable connection is critical, and any interruptions can stop model training, which is unacceptable for projects with tight deadlines. Long-term Costs: With intensive use, rental can cost more than purchasing local GPU. Case: A startup tests models on a cloud server with Tesla H100, saving $30,000 on GPU purchase and quickly adapting to project changes. However, for long-term tasks, they plan to transition to local A6000s to reduce costs. Conclusion Choosing a graphics card for neural networks and ML in 2025 depends on your tasks. Beginners should choose NVIDIA RTX 4060 Ti, which will handle educational projects and basic models. For the mid-segment, A5000 is a good solution, especially if you work with generative models and more complex tasks. For business and large research, Tesla A6000 remains the optimal choice, providing high video memory volume and performance. NVIDIA provides the best graphic cards for AI and maintains leadership thanks to the CUDA ecosystem and specialized Tensor Cores. However, AMD is gradually strengthening its position, offering ROCm support and more affordable solutions, making the GPU market for ML and AI increasingly competitive.