Mastering software engineering empowers practical Artificial Intelligence (AI) utilisation, as AI fundamentally relies on software code; selecting the suitable AI technology stack is vital for business and productivity automation.
Understanding AI’s underlying algorithms and data structures is essential for creating robust and efficient solutions. Developers and researchers can leverage this knowledge to develop bespoke AI applications that can significantly boost operational efficiency and drive innovation.
Software engineering comprises three components: software environment, business stack, and code. Similarily, a good developer would analyse at least three contrasting technology stacks to introduce AI-driven automation.
A typical technology stack for a small services-based business would look like this:
# AI Technology Stack Overview:
# Environment:
# Development: Linux, Unix, Windows, MacOS, Docker.
# Production: Cloud platforms include Azure, AWS, Google Cloud,
# Digital Ocean, plus any light dedicated servers.
# Programming Languages:
# - Includes C/C++, Python, Java, .NET, JavaScript, Ruby, and PHP.
# - Ensures versatility across different software projects.
# Version Control:
# - Utilises Git, GitHub, GitLab, Bitbucket, and Mercurial.
# - Facilitates effective source code management.
# Databases:
# - Incorporates PostgreSQL, MySQL, MongoDB, Redis, and Cassandra.
# - Offers robust data storage and management solutions.
# User Interfaces:
# - Dynamic interaction through HTML, CSS, and
# - JavaScript or static site engines like Hugo.
# - Enhances frontend and backend user experiences.
# Payment Gateways:
# - Integrates Stripe, PayPal, WorldPay, and Adyen for transactions.
# - Provides secure and reliable financial processing.
# Communication Channels:
# - Expands to the Web, mobile apps, and Twilio for diverse outreach.
# - Ensures voice, text, WhatsApp capabilities and alternatives.
# Analytics and Conversation:
# - Employs Google Analytics and chatbots for insights and interaction.
# - Enhances user engagement and data analysis.
# QA and Testing:
# - Adopts Selenium, JUnit, and TestNG for comprehensive testing.
# - Ensures software reliability and performance.
# Additional Libraries:
# - It enriches with NLP, LLM, ML, data analysis, and visualisation.
# - Advances AI capabilities with intent and sentiment detection.
# Security and Compliance:
# - Implements OAuth for authentication and GDPR for data protection.
# - Prioritises user privacy and cyber security standards.
# Cloud Services and Infrastructure Management:
# - Droplets and Kubernetes are for container orchestration,
# - Terraform is for IaC,
# - Streamlines application management and scalability.
To harness AI, organistaions must adopt a streamlined technology stack. Utilising minimal tools maximises AI’s potential, improving efficiency and utility.
Explore top-tier AI technology stacks and GitHub repositories, focusing on open-source projects in nlp, chat, backend systems, frontend funnels, apps, mobile apps, visualisation, machine learning, data science, and Mini-CRM systems.
These resources are specifically designed to boost the efficiency of developers and researchers, facilitating AI-driven automation for business use. By leveraging these advanced technology stacks, businesses can unlock powerful automation capabilities, streamline operations, and enhance customer engagement. This curated selection supports the development and research communities deploying innovative AI solutions.
A well-configured software environment ensures applications run consistently, embodying best practices by minimising compatibility issues. Containerisation and virtualisations are pivotal for robust environments, offering efficient application management and resource utilisation. These technologies streamline deployment, enhance team collaboration, and accelerate software delivery.
# Software Environment Setup
# A. Containerisation
- Overview: Packages software and its operating environment into a container
- Use Case: Ideal for deploying and managing microservices,
enhancing portability and efficiency
- Tools:
- Docker
- Kubernetes
- OpenShift
- Docker Swarm
- Rancher
Docker Github:
https://github.com/docker/compose
https://github.com/docker/getting-started
For virtualisation tools
### Virtualisation Tools
# B. Virtualisation
- Overview: To run multiple operating systems on one physical server
- Use Case: Suitable for development environments or server consolidation
- VMware vSphere: Premier platform for virtualised infrastructure
https://www.vmware.com/products/vsphere.html
- Oracle VirtualBox: Open-source for diverse OS virtualisation
https://www.virtualbox.org/
- Hyper-V: Windows-based solution for virtual machines
https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/about/
- KVM: Linux kernel virtual machine technology
https://www.linux-kvm.org/
- Xen: Secure, efficient open-source hypervisor
https://xenproject.org/
Python is a powerful tool for developers due to its simplicity, readability, elegant syntax, dynamic typing, and a vast collection of libraries and frameworks. Python’s ecosystem is versatile and capable of adapting across diverse computing disciplines, from web applications with Django and Flask to complex data analysis with NumPy, Pandas and pioneering AI innovations through TensorFlow and PyTorch.
Python’s active community enriches its ecosystem, making it adaptable for beginners and experienced developers.
Python: Comprehensive libraries for AI and data analysis
https://www.python.org/about/gettingstarted/
### Python Run & Compile
- Env: Python Interpreter
- Compile: Interpreted, JIT via PyPy
- JIT instantly translates code to run faster
# Python Installation
sudo apt-get update
sudo apt-get install python3
# Django: Web development, full-stack
# Install Django:
pip install django
# Flask: Web development, lightweight
# Install Flask:
pip install flask
# ML & Data Science Libraries
# NumPy: Data science, numerical computations
# Install NumPy:
pip install numpy
# Pandas: Data science, data manipulation
# Install Pandas:
pip install pandas
# TensorFlow: Machine learning, deep learning
# Install Tensorflow:
pip install tensorflow
# PyTorch: Machine learning, neural networks
# Install PyTorch:
pip install torch torchvision torchaudio
Python is central to driving innovations and solutions in data science, machine learning, web development, and automation. To get started with Python and its vast ecosystem, here are some basic terminal commands for installation:
R is a potent tool for statistical computing, data analysis, and visualisation. Its comprehensive packages make it an essential resource for data scientists and statisticians.
The R ecosystem, supported by CRAN, includes popular libraries like ggplot2, dplyr, shiny, and caret for data visualisation, manipulation, web apps, and machine learning.
R’s community continually improves capabilities for research and industry.
R: Statistical analysis and graphics.
R: Statistical computing and graphics
https://www.r-project.org/other-docs.html
### R Run & Compile
- Env: R Interpreter
- Compile: Interpreted, some JIT options
# Install R
sudo apt-get update
sudo apt-get install r-base
# R Packages
# ggplot2: Data visualisation
install.packages("ggplot2")
# dplyr: Data manipulation
install.packages("dplyr")
# shiny: Interactive web apps
install.packages("shiny")
# caret: Machine learning
install.packages("caret")
Java is a versatile programming language for building enterprise-scale apps, web servers, mobile apps, and desktop applications. Java is an object-oriented language around objects, allowing developers to structure code more organised and modularly.
Java is highly portable and provides developers with powerful tools like Spring, Apache Struts and Hibernate.
Java’s platform independence through the JVM (Java Virtual Machine) allows its applications to run on any device or operating system, making it highly portable and widely adopted.
Java is a compiled language, while JavaScript is interpreted.
Java is Known for its robustness
and portability in application development.
https://docs.oracle.com/en/java/
### Java Run & Compile
- Env: Java Virtual Machine (JVM)
- Compile: Compiled to bytecode, runs on JVM
# Installation Instructions for Java
To install Java on your system, follow these steps:
1. Update your package index:
sudo apt-get update
2. Install the default Java Development Kit (JDK):
sudo apt-get install default-jdk
Once installed, verify the installation by:
java -version
// Sample Java code for a simple "Hello, World!" program
public class HelloWorld {
public static void main(String[] args) {
System.out.println("Hello, World!");
}
}
Integral for creating dynamic and interactive web pages, JavaScript thrives with broad browser compatibility and a vast ecosystem of libraries. However, its loosely typed nature can lead to complex debugging processes.
- JavaScript
https://developer.mozilla.org/en-US/docs/Web/JavaScript
### JavaScript Run & Compile
- Env: Browser's JS engines (V8, SpiderMonkey)
- Compile: Interpreted in browsers/Node.js
C Sharp favoured for its robustness in developing a wide range of applications and seamless integration with the .NET framework, C# ensures type safety and scalability. Its platform dependency on Windows, however, could be restrictive.
- C#
https://docs.microsoft.com/en-us/dotnet/csharp/
### C# Run & Compile
- Env: .NET Runtime / .NET Core Runtime
- Compile: Compiled to Intermediate Language (IL)
- JIT compiled to native code at runtime
Celebrated for its unparalleled performance and control over system resources, C++ is ideal for developing game engines and software requiring high-efficiency execution. The complexity of its syntax, though, demands a steep learning curve.
- C++
https://isocpp.org/
### C++ Run & Compile
- Env: System OS
- Compile: Compiled to machine code
Enhancing JavaScript with static typing, TypeScript offers improved code quality and predictability, particularly in large-scale applications. Nonetheless, the initial setup introduces an additional layer of complexity.
- TypeScript
https://www.typescriptlang.org/
### TypeScript Run & Compile
- Env: Depends on target platform (browsers, Node.js)
- Compile: Transpiled to JavaScript
Ruby’s elegant syntax and the robust Rails framework facilitate rapid web development and prototyping. Yet, runtime performance can lag, especially in comparison to compiled languages.
- Ruby
https://www.ruby-lang.org/en/
### Ruby Run & Compile
- Env: Ruby Interpreter (MRI, JRuby, etc.)
- Compile: Interpreted, with some
- JIT options in recent versions
As the cornerstone of modern programming, C offers direct manipulation of hardware and efficient execution of applications. However, its lack of built-in safety features poses a risk of errors and vulnerabilities.
- C
https://en.wikipedia.org/wiki/C_(programming_language)
Note: C doesn't have official website
### C Run & Compile
- Env: System OS
- Compile: Compiled to machine code
Swift’s modern syntax and focus on safety make iOS and macOS development more intuitive and less prone to errors. Limited use outside Apple’s ecosystem could be a drawback.
- Swift
https://swift.org/
### Swift Run & Compile
- Env: Apple platforms, Linux
- Compile: Compiled to optimized native code
Once the primary language for Apple applications, Objective-C provides direct access to C libraries and runtime flexibility. Its syntax and legacy status, compared to Swift, may deter new developers.
- Objective-C
https://developer.apple.com/library/archive/documentation/Cocoa/Conceptual/ProgrammingWithObjectiveC/Introduction/Introduction.html
### Objective-C Run & Compile
- Env: Apple platforms
- Compile: Compiled to machine code, with runtime for dynamic typing
Scala offers a blend of object-oriented and functional programming paradigms, ensuring concise code and immutability. The complexity of its features, however, can intimidate beginners.
- Scala
https://www.scala-lang.org/
### Scala Run & Compile
- Env: Java Virtual Machine (JVM)
- Compile: Compiled to Java bytecode
Go known for its simplicity and efficient concurrency handling, Go (or Golang) is optimised for modern computing architectures. Its simplicity, paradoxically, limits expressiveness in complex applications.
- Go
https://golang.org/
### Go Run & Compile
- Env: System OS
- Compile: Compiled to machine code
Kotlin’s safety features and interoperability with Java make it a favoured choice for Android development, reducing boilerplate code. Compared to Java, the smaller community might affect learning and troubleshooting resources.
- Kotlin
https://kotlinlang.org/
### Kotlin Run & Compile
- Env: JVM, Android, Native platforms
- Compile: Compiled to Java bytecode or native binaries
Rust guarantees memory safety without a garbage collector, ideal for system programming and high-performance applications. Its steep learning curve and meticulous ownership model can be challenging for newcomers.
- Rust
https://www.rust-lang.org/
### Rust Run & Compile
- Env: System OS
- Compile: Compiled to machine code
Dart excels in developing visually attractive and natively compiled multi-platform applications, especially with the Flutter framework. Dart’s niche adoption outside Flutter development is a notable limitation.
- Dart
https://dart.dev/
### Dart Run & Compile
- Env: Dart VM, browsers (via compilation to JavaScript), Flutter
- Compile: JIT in development, AOT for production
Perl’s text processing capabilities and comprehensive CPAN repository make it powerful for scripting and backend development. However, its syntax, described as cryptic, complicates readability and maintenance.
- Perl
https://www.perl.org/
### Perl Run & Compile
- Env: Perl Interpreter
- Compile: Interpreted
Lua’s lightweight footprint and embeddability perfectly suit game development and embedded systems. Its limited standard library, requiring external modules for everyday tasks, is a drawback.
- Lua
https://www.lua.org/
### Lua Run & Compile
- Env: Lua Interpreter, embedded in applications
- Compile: Interpreted, JIT via LuaJIT
Haskell’s pure functional programming model encourages high code correctness and abstraction. However, the significant paradigm shift from imperative programming presents a considerable learning obstacle.
- Haskell
https://www.haskell.org/
### Haskell Run & Compile
- Env: System OS via Glasgow Haskell Compiler
- Compile: Compiled to machine code
.NET is a free, open-source platform by Microsoft for building various applications. It works across the web, mobile, desktop, games, and IoT. You can use multiple languages, editors, and libraries. The framework is popular due to its comprehensive ecosystem, robust security, and regular updates.
- .NET
https://dotnet.microsoft.com/
### .NET Run & Compile
- Env: .NET Runtime / .NET Core Runtime
- Compile: Compiled to Intermediate Language (IL),
- JIT compiled to native code at runtime
It is a preferred choice for enterprise-level projects due to its scalability and high-performance capabilities. However, developers heavily invested in other technology stacks may face limitations due to its close association with the Microsoft ecosystem.
Flutter is an open-source UI development kit by Google used to create apps for multiple platforms from a single codebase.
- Flutter
https://flutter.dev/
### Flutter Run & Compile
- Env: Dart VM for development; iOS and Android for production
- Compile: AOT compiled to native code for production
Flutter is fast, flexible, and offers native performance. It creates visually attractive, natively compiled applications quickly. The learning curve for Dart and the platform’s young age may be minor drawbacks.
COBOL, the cornerstone of business computing, is integral to financial systems globally. Its strength lies in processing extensive transactions reliably, not in processing speed, marking its significance in the banking industry where precision is paramount.
- COBOL
https://www.microfocus.com/en-us/products/cobol-development-and-mainframe-solutions/overview.
### COBOL Run & Compile
- Env: Mainframe systems, COBOL runtime environments
- Compile: Compiled to machine code
Cobol’s resilience is supported by platforms like Micro Focus, ensuring COBOL’s compatibility with modern technologies.
FORTRAN remains a stalwart in scientific computing, revered for its precision in numerical analysis and computation. It’s a key player in fields demanding mathematical calculations, supporting groundbreaking research and explorations.
- FORTRAN
https://fortran-lang.org
### FORTRAN Run & Compile
- Env: System OS
- Compile: Compiled to machine code
Despite the emergence of newer programming languages, FORTRAN’s specialised libraries, like BLAS and LAPACK, underscore its ongoing importance in scientific domains. Fortran Lang offers comprehensive resources for the latest on FORTRAN’s evolution and its community.
Pascal shines in educational contexts, promoting strong programming fundamentals that have informed the design of contemporary languages. Its legacy endures through the Free Pascal Compiler, bridging Pascal’s principles with modern cross-platform development.
- Pascal
https://www.freepascal.org.
### Pascal Run & Compile
- Env: Depends on compiler
- (e.g., Free Pascal Compiler)
- Compile: Compiled to machine code or bytecode
Though its professional application has waned, Pascal’s emphasis on clarity and structure continues to influence new generations of programmers and software engineers.
PHP powers billions of websites, including WordPress, Drupal and Joomla. It’s easy to use, flexible, and has powerful frameworks like Laravel and Symfony.
PHP frameworks have transformed web development with elegant syntax, robust features, and comprehensive tools for routing, authentication, and caching tasks.
- PHP
https://php.net/
### PHP Run & Compile
- Env: Web servers with PHP installed (e.g., Apache, Nginx)
- Compile: Interpreted
- Laravel
https://laravel.com/
- Symfony
https://symfony.com/
- Zend (Now Laminas Project)
https://getlaminas.org/
Laravel and Symfony are popular PHP frameworks that contribute to PHP’s role in the modern web. Both frameworks make it easier to integrate AI into PHP projects. Tools are available to leverage PHP’s scripting capabilities for data-driven features.
PHP is an evolving programming language that adapts to new challenges. Recent updates focus on improving performance, security, and modern language features. The PHP community fosters a robust ecosystem for developers, making it a key player in web development.
Python, PHP, Java, R, and Ruby stand out in software development, each tailored to specific tasks: Python excels in AI and data science; PHP dominates web development; Java is universal, especially for enterprise; R leads in statistical analysis; Ruby offers simplicity in web projects. Their unique strengths guide developers in choosing the right tool for their project’s goals and team skills, ensuring a blend of innovation and practicality.
Swift emerges as a critical player in Apple app development, offering a modern, fast, and safe programming environment. Swift’s compatibility with Apple’s ecosystem allows developers to leverage native libraries and tools for creating intuitive and high-performance iOS applications.
Kotlin, recognised for its safety and conciseness, is the counterpart for Android app development. It provides a seamless development experience, reducing the boilerplate code and enhancing app performance and maintainability on Android devices.
On the web development front, PHP and .NET provide robust backend solutions, facilitating server-side scripting and application logic for web applications. PHP’s wide use in web development is complemented by. NET’s framework for building efficient, secure web apps and services.
Lastly, Flutter introduces a modern framework for developing natively compiled mobile, web, and desktop applications from a single codebase. Its ability to deliver high-quality, multi-platform applications with a single development process makes it an invaluable tool for seamlessly extending software across various platforms.
In software development, databases are crucial architectural elements influencing application functionality and user experience. They facilitate electronic storage, manipulation, and information retrieval, making them indispensable for various systems. Database Management Systems (DBMS) ensure data remains organised and accessible, bolstering operational efficiency and scalability across diverse sectors.
Choosing a DBMS is critical and impacts an application’s architecture and future development. Here are premier DBMS options with unique capabilities and use cases in software development.
PostgreSQLis a highly versatile and compliant SQL database. It excels at managing complex data workloads and large datasets, making it ideal for enterprise-grade applications.
sudo apt-get update
sudo apt-get install postgresql postgresql-contrib
MongoDB is known for its schema-less data model that enables flexibility and rapid development, especially in big data and applications requiring scalable document storage.
wget -qO - https://www.mongodb.org/static/pgp/server-4.4.asc | sudo apt-key add -
echo "deb http://repo.mongodb.org/apt/ubuntu bionic/mongodb-org/4.4 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-4.4.list
sudo apt-get update
sudo apt-get install -y mongodb-org
MySQL is a reliable and efficient option for managing simple data storage but has limitations under high-load conditions.
sudo apt-get update
sudo apt-get install mysql-server
Redis is great for rapid data access like caching and real-time analytics, but not ideal for persistent data management due to its reliance on memory storage.
sudo apt-get update
sudo apt-get install redis-server
SQLite is a lightweight embedded database ideal for mobile and small web projects, but lacks concurrent processing capabilities. Evaluate DBMS based on data model complexity, scalability demands, and specific functional requirements of the application.
sudo apt-get update
sudo apt-get install sqlite3
Neo4j provides powerful graph database functionalities, while Couchbase offers versatile NoSQL options for web and mobile apps.
Neo4j for Linux: official package.
More options on their website.
https://neo4j.com/docs/operations-manual/current/installation/
Cassandra is good for scalability in distributed settings. MariaDB is recommended for advanced features. Oracle and SQL Server are suitable for enterprise data management.
Cassandra can be installed on Linux systems via the command line.
https://cassandra.apache.org/_/download.html
Amazon’s DynamoDB is a fully managed NoSQL service designed for high performance and scalability in cloud environments.
DynamoDB is accessible as a cloud service.
https://aws.amazon.com/dynamodb/.
Please explore our collection on getting started with software engineering, covering essential development and collaboration tools.