Software Engineering
Software Engineering
A powerful AI model is just the beginning. Software engineering is the discipline that transforms a successful experiment into a robust, maintainable, and scalable product. It involves applying rigorous principles of design, testing, and architecture to ensure the final application is not just intelligent, but also reliable and easy to evolve over time.
Key Technologies & Frameworks:
Python: The primary language for AI development, supported by a rich ecosystem of scientific and machine learning libraries.
JavaScript: For building interactive web applications, enabling AI models to be deployed in user-friendly interfaces. And derivate frameworks such as: Next.js, Node.js, React: A JavaScript library for building user interfaces, enabling the creation of dynamic and responsive web applications.
Java: For building scalable and high-performance applications, especially in enterprise environments.
Docker & Podman: Containerization technology for packaging applications and their dependencies into portable containers, ensuring consistency across environments.
Kubernetes: For orchestrating containerized applications, enabling automated deployment, scaling, and management of applications in cloud environments.
Git, GitHub & GitLab: The standard for version control and collaborative development, essential for managing codebases and CI/CD pipelines.
CI/CD: Automation pipelines for continuous integration and continuous delivery, ensuring that code changes are automatically tested and deployed.
GitOps: A methodology for managing infrastructure and application deployments using Git as the single source of truth, enabling version control for both code and configuration. Allowing to release software updates with the blue/green paradigm.
Argo Workflows: For orchestrating complex workflows and data pipelines, enabling the automation of machine learning tasks from data ingestion to model deployment.
FastAPI: For building high-performance, production-ready APIs to serve machine learning models over the web.
Databases: PostgreSQL, MongoDB, Redis and others. For storing and retrieving both structured and unstructured data, from user information to model artifacts and training datasets.
Flutter: For building cross-platform mobile native, desktop, and web applications, enabling AI models to be integrated into mobile devices with a single codebase.
Key Projects:
Example Of Complex Software Engineering Design: This is team project that showcases the application of software engineering principles in building a complex system. It includes a detailed UML architecture diagram, design patterns, and best practices for developing scalable and maintainable software in Java with Maven. Code quality assured with Testing and SonarQube.