Data Processing Unit (DPU): Meaning & How It Works

An SLA is an agreement that defines the level of service a company provides to its customers. This term is usually used in IT and telecommunications.  Unlike standard service contracts, a Service Level Agreement provides a very detailed description of service quality, operating modes, response times to incidents, and other parameters. Main Characteristics of an SLA A Service Level Agreement usually has the following characteristics: Maximum possible transparency of all processes and interactions between the service provider and the client. When drafting the contract, vague wording that could be interpreted ambiguously in one direction or another is avoided. Clearly defined rights and obligations understood by all participants in the agreement. For example, a provider commits to ensuring 99.9% service availability and to pay compensation if a lower figure is recorded, while the client has the right to request that compensation. Expectation management. For instance, a client might expect 24/7, ultra-fast support even for minor issues, while the provider cannot offer such a service. In this case, the client should either lower their expectations or sign a contract with another provider. A third option is also possible: the provider may raise the service level if it benefits their business processes. The agreement specifies the timeframes for fixing issues and solving other problems. It also describes possible compensations that the client may receive if the company fails to meet the declared metrics. An SLA does not always need to be a large document. The main thing is that it clearly describes the core parameters of the service in understandable terms. For example, the AWS S3 SLA is only one page long. It lists monthly uptime percentages and the amount of compensation the client receives if the service fails to meet those thresholds. What is Usually Included in an SLA The example above from Amazon Web Services is not a standard; it is just one possible format tailored to a specific service. An IT SLA often includes the following sections: The procedure for using the service. Responsibilities of both parties, including tools for mutual monitoring of performance. Specific steps for troubleshooting and restoring functionality. The agreement may also specify its term. In some cases, the parties describe in detail the procedure for adding new requirements for functionality or service availability. When describing service quality, its parameters are also disclosed. These typically include: Service availability. Response time to a problem. Time to fix incidents. The SLA may also specify a metric for operating hours. When describing payment procedures, it may indicate the billing model (e.g., pay-as-you-go, fixed rate, etc.). If penalties are provided, the SLA will specify the situations in which the provider must pay them. If the client is entitled to compensation, the SLA also describes the relevant situations and payment procedures. Key SLA Parameters SLA parameters are metrics that can be measured. The agreement should not contain vague phrases like “issues will be resolved quickly, before you even notice.” Such wording is unclear and prevents all participants from organizing proper workflows. For example, the support schedule metric should clearly define when and for which groups of users technical support is available. Suppose a company divides its clients into several groups: Group 1: 24/7 phone and chat support. Group 2: phone and chat support only on weekdays. Group 3: chat-only support on weekdays. Metrics are necessary so that all participants understand which services they receive, when, and in what scope. From this, several key characteristics follow: Metrics must always be publicly available. Their descriptions must be unambiguous for all parties. Clients must be notified in advance about metric changes. When defining metrics, it’s important not to set overly strict requirements, as this significantly increases costs. For instance, suppose a typical specialist can resolve a problem in 4 hours, while a higher-level expert can do it in 2 hours. Writing “2 hours” as the SLA metric is not ideal, as it would immediately make the expert’s work more expensive. If you specify “1 hour,” costs rise further due to the increased risk of penalties for non-compliance. Other important metrics can include response time to a client request. The values may differ depending on the client’s status and problem criticality. For example, a company providing IT outsourcing services might have: Premium clients: response within 15 minutes. Basic clients: response within 24 hours. All of this must be clearly reflected in the SLA. In addition to response time, there’s also incident resolution time. The logic for this parameter is similar: even if a client is important, requests are prioritized based on criticality. For example: If a client’s local office network stops working and all processes halt, that issue must be prioritized. The SLA may state that local network troubleshooting should take no more than 5 hours. If the same client needs to add a few new devices to an already working network, the resolution time may be several hours or even days. The combination of response time and resolution time forms downtime. These and other parameters must be described in the SLA and accepted by all parties before cooperation begins. This approach reduces conflicts; everyone understands what to expect from each other. Service Availability For providers, one of the most important SLA parameters is service availability. It is usually measured in days, hours, or minutes over an agreed period. For example, a provider guarantees that a cloud computing service will be available 99.99% of the time during a year. At first glance, the difference between SLA 99 and SLA 100 may seem small. But in absolute terms, it’s significant. At 99%, you agree that servers may be down up to 4 days per year. At 100%, downtime should be zero—something no company can guarantee. That’s why SLAs are usually written with “nines”: e.g., 99.9%, 99.99%, etc. For example, Hostman.com guarantees 99.98% uptime, meaning total annual downtime will not exceed 1 hour 45 minutes. Some providers promise “five nines”: 99.999% uptime, or less than 15 minutes of downtime per year. But this is not always the best option. Two points to consider: The higher the SLA percentage, the higher the cost. Not every client needs such a high level. In most cases, 99.982% uptime (or slightly higher) is sufficient. It’s important to check not only the number of nines but also the time unit used for measurement. By default, SLA indicators are calculated annually. For example, 99.95% availability equals no more than 4.5 hours of downtime per year. If the contract doesn’t explicitly say that the time unit is “per year,” be sure to clarify, as some providers disguise monthly values as annual. Another key concept is aggregate availability, which equals the lowest of all measured values. Benefits of an SLA Signing and adhering to an SLA benefits both parties. For the company, it defines obligations and protects against unreasonable client demands, such as urgently fixing a minor issue in the middle of the night. Other benefits include: The provider can use the SLA to organize both external and internal processes, such as introducing different support levels depending on service criticality and client importance. Clients gain clarity about what services they can expect, in what timeframes, and in what order, helping them plan their core operations. SLA vs. SLO: What’s the Difference An SLA can also be viewed as an indicator of user satisfaction, ranging from 0% to 100%. Absolute satisfaction (100%) is impossible, just as it’s impossible to guarantee 100% uptime. Therefore, when choosing metrics, one should be realistic and select achievable values. For example, if your team doesn’t provide 24/7 support, you shouldn’t promise it. When the team expands, you can update the SLA and delight clients by offering round-the-clock assistance. To monitor service levels internally, another system is used: SLO (Service Level Objective). These are the target values the provider aims to achieve. Example: Current capabilities are handling 50 tickets per business day, working 9:00 to 18:00, five days a week. These metrics are fixed in the SLA and shown to clients. Meanwhile, the SLO document sets internal goals, for example, increasing the number of handled tickets to 75 per day or switching to 24/7 support. This directly affects the company’s future service level. How to Create a Proper SLA Start with a descriptive section, which usually includes: A glossary System description Participant roles (users, support specialists) Boundaries of operation: geography, time, functionality The next section describes the services provided, giving the client a full understanding of what they can expect when signing with the provider. Then comes the main section, describing the service level. It should include metrics that reflect quality and are easily measurable, as well as metric values that are specific numbers guiding all participants. You can end the SLA with references to other documents that regulate service processes. At all stages of preparing an SLA, remember: it is a regulatory document. Its main goal is control. The more control over the process, the better the SLA. If there is no control, such an agreement is meaningless. Checklist: What to Consider When Preparing an SLA If you are not signing but drafting an SLA to offer clients, pay attention to the following points: Users. In large systems, divide users into groups and manage them separately. This helps allocate resources efficiently and avoid overload from different client types. Services. Consider the criticality of each service for each client group. Example: You provide a CRM to trading companies. If they can’t use it, they lose money and complain, meaning it’s a high-criticality service. Printer replacement or user account creation can wait until tomorrow. Service quality parameters. They must align with business goals and client needs. A typical example is incident resolution times, e.g., 24/7 support versus 9 a.m. to 5 p.m. on weekdays only. An SLA is a document that must be announced to all users whenever it is introduced or updated, regardless of privilege level or service criticality. SLA is a management tool that constantly evolves. You may find that current quality parameters harm business processes or no longer meet client expectations. In that case, management should decide to optimize processes or improve services. The main goal of SLA indicators is not to attract users but to ensure open dialogue with them. Every participant accepts the agreement and commits to following it. Violation of an SLA is grounds to claim compensation and terminate cooperation.

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.