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Software Automation
Syllabus Content
Syllabus Content
Distinguish between artificial intelligence (AI) and ML
Investigate how machine learning (ML) supports automation through the use of DevOps, robotic process automation (RPA) and business process automation (BPA)
Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine Learning
Artifical Intelligence
Artifical Intelligence
There are some disagreements between artificial intelligence (AI) experts about how to define what artificial intelligence is (or is not) and how to classify/organise diverse areas of AI. One such defintion might be:
"Artificial Intelligence refers to computer systems designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Rather than following explicit programming instructions, AI systems can adapt their behaviour based on the data they analyse."
Here are a couple of videos that explore the definition of AI:
Machine Learning
Machine Learning
Machine Learning is a specific subset of AI that focuses on creating algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience (data) rather than being explicitly programmed with rules. ML is the engine that powers many modern AI applications.
Further reading: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
📖 In your workbook: In your own words, distinguish between artificial intelligence (AI) and machine learning (ML).
How ML Supports Automation
How ML Supports Automation
Machine learning (ML) has revolutionised how we approach automation in software development and business operations. In this lesson, we'll explore three major types of automation that leverage ML to improve efficiency, reduce errors, and optimise processes.
Business Process Automation (BPA)
Business Process Automation (BPA)
Definition
Business Process Automation (BPA) refers to the use of technology to automate complex business processes to improve efficiency, reduce errors, and standardise workflows.
Links
Key Characteristics
Automates entire business processes from end to end
Often involves multiple departments or business functions
Uses various technologies including ML to make processes smarter
Aims to improve overall business operations
Discussion Questions
What is a business process? Think of three examples of business processes in a typical company.
How does machine learning enhance BPA? Provide a specific example.
What's the difference between workflow automation and BPA?
Robotic Process Automation (RPA)
Robotic Process Automation (RPA)
Definition
Robotic Process Automation (RPA) is a technology that uses software robots ("bots") to automate routine, repetitive tasks typically performed by humans interacting with digital systems.
Links
Key Characteristics
Software robots mimic human actions within digital systems
Focuses on specific, repetitive tasks rather than entire processes
Bots learn by observing human actions in the GUI
A subset of BPA that specialises in task-level automation
Enhanced by ML to handle more complex cases and decisions
Discussion Questions
How is RPA different from physical robots? Why is this distinction important?
Think of three specific office tasks that would be ideal candidates for RPA.
How does machine learning extend the capabilities of RPA bots?
DevOps
DevOps
Definition
DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle and provide continuous delivery of high-quality software.
Key Characteristics
Integrates development and operations teams
Focuses on automation, collaboration, and continuous improvement
Uses ML to optimise testing, deployment, and monitoring
Aims to deliver software updates faster and more reliably
Discussion Questions
What problems in traditional software development does DevOps aim to solve?
Atlassian's Jira is a DevOps tool developed in Sydney. Research and describe how it supports DevOps practices.
Explain how machine learning can be applied to improve at least two specific DevOps processes.
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