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Our Tech Stack
AIUK's Tech Stack
This tech stack outlines the tools, technologies, and frameworks that AIUK employs across its operations. Each component is chosen to ensure scalability, reliability, security, and efficiency, supporting AIUK’s mission to deliver cutting-edge AI solutions and services.
Our Technology
Operating Systems
Windows: Used across development environments, particularly for enterprise applications and software requiring Microsoft integration.
Linux: Preferred for server environments due to its stability, security, and open-source nature. Essential for cloud deployments and AI model training.
Android: Platform for mobile application development, particularly for AI-driven apps and services.
iOS: Used for developing mobile applications on Apple devices, ensuring a seamless user experience in AIUK’s mobile solutions.
Mac OS: Commonly used by developers for software development, particularly in design and iOS application development.
Programming Languages
Python: Primary language for AI and machine learning development, leveraging its ecosystem of libraries like TensorFlow, PyTorch, and scikit-learn.
JavaScript (Node.js): Used for building scalable, real-time backend services and RESTful APIs.
Java: Employed for enterprise-level applications, especially where stability and performance are crucial.
R: Utilised for statistical analysis and data visualisation in research-oriented projects.
C++: Applied in performance-critical components, particularly in machine learning models requiring optimisation.
Prolog: Used in AI applications requiring logic programming, particularly in knowledge representation and problem-solving.
AI and Machine Learning Frameworks
TensorFlow: For building and deploying deep learning models. Supports complex computations and large-scale machine learning tasks.
PyTorch: Preferred for research and development due to its flexibility and ease of use, particularly in experimental AI models.
Keras: A high-level API built on TensorFlow, used for quick prototyping of neural networks.
Apache Jena: Framework for building Semantic Web and linked data applications, used in AI-driven knowledge-based systems.
AI Foundation Models
GPT Series: Used for natural language processing tasks, including text generation, summarisation, and translation.
BERT: Employed in NLP for tasks like text classification, sentiment analysis, and question answering.
Jurassic-1 Jumbo: A large-scale language model used for various NLP tasks, offering high accuracy in language understanding.
Llama: A versatile model used for text-based tasks, particularly in multilingual applications.
LaMDA: Specialised in dialog applications, used for developing conversational AI and chatbots.
DALL-E: Utilised for generating images from textual descriptions, useful in creative and design applications.
Stable Diffusion: A text-to-image model used for generating high-quality images, applied in content creation and design.
CLIP: A model that connects vision and language, used for tasks like image classification and zero-shot learning.
Flamingo: Used for visual language understanding, particularly in tasks that combine text and image processing.
Development Frameworks
Django (Python): A high-level web framework used for building robust, scalable web applications.
Flask (Python): A lightweight micro-framework used for creating simple, flexible web applications and microservices.
Express.js (Node.js): A minimalist web framework for Node.js, used for building APIs and backend services.
Spring Boot (Java): Used for creating stand-alone, production-grade Spring-based applications with minimal configuration.
Symfony2: A PHP framework for building web applications, known for its scalability and flexibility.
Laravel: A PHP framework used for elegant and expressive syntax, facilitating rapid application development.
Yii: A high-performance PHP framework, ideal for developing large-scale applications.
CodeIgniter: A lightweight PHP framework used for rapid development with minimal configuration.
CakePHP: A PHP framework that provides a solid foundation for building web applications, offering a flexible database access layer.
Web Application Development
.NET: Used for building secure and scalable enterprise-level web applications, particularly in Microsoft environments.
PHP: Widely used for server-side scripting and building dynamic web applications.
J2EE (Java 2 Platform, Enterprise Edition): Used for developing large-scale, multi-tiered, scalable, and secure enterprise applications.
Ruby on Rails: A web application framework that allows for rapid development using the Ruby programming language.
AngularJS: A structural framework for dynamic web apps, offering a comprehensive solution for front-end development.
React.js: A JavaScript library for building user interfaces, particularly single-page applications with dynamic content.
Web Design UI
HTML5: The core technology for structuring and presenting content on the web.
CSS3: Used for describing the look and formatting of a document written in HTML, essential for responsive design.
Bootstrap 4: A front-end framework used for designing responsive and mobile-first websites quickly.
jQuery: A fast, small, and feature-rich JavaScript library that simplifies HTML document traversal, event handling, and animation.
Content Management & Ecommerce Software
WordPress: A content management system (CMS) used for building and managing websites, blogs, and online stores.
Drupal: A flexible and scalable CMS, often used for large-scale websites and complex content requirements.
Joomla: An open-source CMS for publishing web content, suitable for small to medium-sized businesses.
Magento: A powerful eCommerce platform for building and managing online stores.
Zen Cart: An open-source shopping cart software used for eCommerce websites.
PrestaShop: A freemium, open-source eCommerce solution, ideal for small to medium-sized online businesses.
Databases
MSSQL: Microsoft’s relational database management system, used for enterprise-level applications requiring advanced features like data warehousing and complex queries.
MySQL: A widely-used open-source relational database management system, known for its reliability and ease of use.
Oracle: A robust, scalable database system used for handling large-scale enterprise applications.
MongoDB: A NoSQL database used for handling unstructured data, flexible schema design, and scalability.
SQLite: A lightweight, file-based database used for local storage in applications.
Mobile Technologies
iOS (Objective C, Swift): Used for developing native applications on Apple’s iOS platform, ensuring high performance and integration with Apple’s ecosystem.
Android: For developing native applications on Google’s Android platform, supporting a wide range of devices.
Hybrid: Platforms like Ionic and Cordova used for developing mobile applications that work across both iOS and Android from a single codebase.
Flutter: Google’s UI toolkit for building natively compiled applications for mobile, web, and desktop from a single codebase.
DevOps and CI/CD Tools
Docker: Used for containerising applications, ensuring consistent environments from development to production.
Kubernetes: For automating deployment, scaling, and management of containerised applications, particularly in cloud environments.
Jenkins: A widely-used automation server for building, testing, and deploying applications as part of the CI/CD pipeline.
GitLab CI/CD: Integrated into GitLab, it’s used for continuous integration and continuous delivery of code changes.
Terraform: An Infrastructure as Code (IaC) tool used for building, changing, and versioning infrastructure efficiently and safely.
Software Testing
PHPUnit: A programmer-oriented testing framework for PHP, providing a comprehensive testing suite.
Codeception: A PHP testing framework, used for acceptance, functional, and unit testing.
Selenium: Used for automating web application testing, ensuring that UI elements work as expected.
Appium: An open-source tool for automating mobile apps, used for both Android and iOS platforms.
AI Model Testing
Unitest: A framework for writing and running tests for AI models, ensuring their correctness and performance.
pytest: A testing framework for Python, used for unit and functional testing of AI models.
JUnit: A testing framework for Java applications, used for unit testing and integration testing.
TestNG: A testing framework inspired by JUnit, designed for test configuration, test group management, and more.
Jest: A JavaScript testing framework, particularly useful for testing frontend applications.
nose2: A test runner for Python, extending unittest to make testing easier.
MLflow: A platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment.
Grafana: Used for visualising metrics related to AI model performance and system health.
Prometheus: A monitoring system that collects metrics from AI models and infrastructure, enabling performance tracking.
Project Management Tools
Jira: A project management tool used for tracking tasks, managing sprints, and facilitating Agile workflows.
Pivotal Tracker: An agile project management tool that helps teams build software by tracking progress and keeping everyone on the same page.
Trello: A flexible project management tool used for organising tasks and projects visually on boards.
Podio: A collaborative work platform used for project management and communication.
Basecamp: A project management and team collaboration tool, known for its simplicity and effectiveness in managing projects.