Unit of competency details
ICTAII501 - Automate work tasks using machine learning (Release 1)
Summary
Usage recommendation:
Current
Releases:
| 1 1 (this release) |
03/Feb/2022 |
Companion volumes:
Unit of competency
Assessment requirements
Training packages that include this unit
Qualifications that include this unit
Skill sets that include this unit
Classifications
Classification history
| ASCED Module/Unit of Competency Field of Education Identifier | 020119 | Artificial Intelligence | 27/Apr/2022 | |
The content being displayed has been produced by a third party, while all attempts have been made to make this content as accessible as possible it cannot be guaranteed. If you are encountering issues following the content on this page please consider downloading the content in its original form
Unit of competency
Modification History
|
Release
|
Comments
|
|
Release 1
|
This version first released with the Information and Communications Technology Training Package Version 8.0.
Newly created unit of competency to address in-demand skills needs.
|
Application
This unit describes the skills and knowledge required to use machine learning (ML) principles and techniques to support the automation of procedural tasks and improve organisational productivity.
The unit applies to individuals who may work across a wide range of information and communications technology (ICT) roles, including support technicians, system administrators, programmers and cloud computing engineers.
No licensing, legislative or certification requirements apply to this unit at the time of publication.
Unit Sector
Artificial intelligence
Elements and Performance Criteria
|
ELEMENT
|
PERFORMANCE CRITERIA
|
|
Elements describe the essential outcomes.
|
Performance criteria describe the performance needed to demonstrate achievement of the element.
|
|
1. Organise required ML dataset
|
1.1 Confirm ML work brief and tasks according to organisational policies and procedures
1.2 Compare structured, unstructured, labelled and unlabelled machine training data according to work brief
1.3 Randomise, deduplicate and check machine training data for imbalances and biases
1.4 Analyse unbiased and biased dataset considerations according to work brief
1.5 Divide data into training subset and evaluation subset according to work brief
|
|
2. Review data algorithms
|
2.1 Confirm that data is correctly grouped as labelled or unlabelled
2.2 Analyse regression algorithms, decision trees or neural net algorithms for labelled data, where required
2.3 Analyse clustering, association, instance-based or neural network algorithms for unlabelled data, where required
2.4 Document analysis findings according to organisational policies and procedures
2.5 Select algorithm for dataset according to analysis findings
|
|
3. Create ML model
|
3.1 Confirm expected ML outputs with required personnel
3.2 Run variables through selected algorithm according to work brief
3.3 Compare expected and actual ML outputs
3.4 Adjust algorithm and re-run variables through selected algorithm according to work brief
3.5 Confirm that new algorithm outputs yield accurate output results
3.6 Compare expected and final outputs with required personnel
|
|
4. Use ML model for scoring
|
4.1 Configure ML model into existing systems according to organisational policies and procedures
4.2 Run organisational data through algorithm according to work brief
4.3 Secure and save ML model according to organisational policies and procedures
|
Foundation Skills
This section describes those language, literacy, numeracy and employment skills that are essential to performance but not explicit in the performance criteria.
|
Skill
|
Description
|
|
Reading
|
- Interprets meaning from a range of texts to assist in promoting work-related ML
|
|
Writing
|
- Uses appropriate vocabulary, grammatical structure and conventions when developing documentation
|
|
Oral communication
|
- Asks questions and actively listens to share and compare outputs with others
- Explains information using structure and language appropriate to audience
|
|
Problem solving
|
- Applies problem-solving processes to identify actions required to support organisational productivity
|
Unit Mapping Information
No equivalent unit. Newly created unit.
Links
Companion Volume Implementation Guide is found on VETNet - - https://vetnet.gov.au/Pages/TrainingDocs.aspx?q=a53af4e4-b400-484e-b778-71c9e9d6aff2
Assessment requirements
Modification History
|
Release
|
Comments
|
|
Release 1
|
This version first released with the Information and Communications Technology Training Package Version 8.0.
Newly created unit of competency to address in-demand skills needs.
|
Performance Evidence
The candidate must demonstrate the ability to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including evidence of the ability to:
- develop at least one machine learning (ML) model to automate organisational work task
- use an algorithm to produce variable outputs on at least two occasions.
In the course of the above, the candidate must:
- adapt ML principles and techniques to suit specific organisational problems
- apply required organisational policies and procedures.
Knowledge Evidence
The candidate must be able to demonstrate knowledge to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including knowledge of:
- tasks and processes commonly automated in similar organisations, including:
- creating and managing email campaigns
- using chatbots and automated messaging platforms
- analysing trends within datasets
- hiring and recruitment
- employee help desk support services
- generating customer support logs and tickets
- common organisational processes and technologies where ML principles can be applied to improve productivity
- industry-recognised ML principles and techniques
- functions and features of machine training datasets in relation to automating work tasks
- characteristics and functions of structured, unstructured, labelled and unlabelled data
- characteristics of unbiased and biased datasets
- processes for generating randomised, deduplicated and unbiased data
- differences between training subsets and evaluation subsets
- key algorithms used to run labelled data, including:
- regression algorithms
- decision trees
- instance-based algorithms
- neural network algorithms
- key algorithms used to run unlabelled data, including:
- clustering algorithms
- association algorithms
- neural network algorithms
- processes for operating and running variables through algorithms
- characteristics of semi-supervised, supervised, unsupervised and reinforcement learning
- basic functions and operations of common programming languages for algorithms
- characteristics of key logic in algorithms
- method to compare expected and actual ML outputs
- secure and safe practices to develop ML models in organisational contexts
- key methods to determine ML deployment requirements for end users, including:
- cross-industry standard process for data mining (CRISP-DM) methodology
- software development methodology
- organisational policies and procedures, legislative requirements and frameworks relating to work tasks, including:
- behavioural science
- data governance
- ethics
- human rights
- Australia’s Artificial Intelligence Ethics Framework.
Assessment Conditions
Skills in this unit must be demonstrated in a workplace or simulated environment where the conditions are typical of those in a working environment in this industry.
This includes access to:
- organisational processes and technologies where ML principles can be applied to improve productivity
- work brief, organisational policies and procedures, legislative requirements and frameworks required to demonstrate the performance evidence.
Assessors of this unit must satisfy the requirements for assessors in applicable vocational education and training legislation, frameworks and/or standards.
Links
Companion Volume Implementation Guide is found on VETNet - - https://vetnet.gov.au/Pages/TrainingDocs.aspx?q=a53af4e4-b400-484e-b778-71c9e9d6aff2
