Online
AWS machine learning
Duration
10 hrs
Level
Deep skilling
Delivery
Self-paced online
Assessment
AWS
Certificate
AWS
Overview
Training a model on a laptop is a tutorial. Getting one into production, monitored, secured and governed, is a job. This journey is built around the second thing.
It takes you from machine learning fundamentals through to deploying and operating models on AWS, structured as a progression rather than a pile of disconnected courses. You cover the ML lifecycle end to end: ingesting and cleaning data, engineering features, choosing an algorithm that fits the problem, training and evaluating in Amazon SageMaker, then deploying, monitoring and optimising what you have built. Alongside that you work through the parts that matter once a model touches real users, including fairness, bias, explainability, security and compliance.
The curriculum integrates multiple AWS digital training modules into a single pathway and is aligned to National Occupational Standards under the Deep Skilling category, so the outcomes are recognised rather than self-declared. It also builds the core competencies behind AWS role-based certifications in machine learning and AI.
What you will learn
By the end of the journey you will be able to:
Understand the fundamentals of machine learning
- Explain core ML concepts, the types of learning and the ML development lifecycle
- Recognise common ML use cases across industries
Build foundational AWS skills for machine learning
- Explore AWS services for data preparation, model building, training and deployment including Amazon SageMaker, AWS Lambda, Amazon S3 and AWS Glue
- Navigate AWS ML tools and apply them to basic business problems
Work with data for machine learning
- Ingest, clean and prepare datasets using AWS tools
- Apply feature engineering techniques to improve model accuracy
Develop and train models
- Build, train and evaluate ML models in Amazon SageMaker
- Select appropriate algorithms based on the problem statement
Deploy and operationalise models
- Deploy trained models to production using AWS ML services
- Monitor and optimise deployed models for scalability and efficiency
Apply responsible AI and ML best practice
- Understand fairness, ethics, bias and explainability in machine learning
- Apply security and compliance best practices when handling data on AWS
Demonstrate industry readiness
- Solve real-world ML scenarios using AWS tools
- Prepare for AWS certification pathways in machine learning
Prerequisites
Basic programming knowledge is recommended but the journey is structured to accommodate learners without a deep technical background. It is designed for:
- Students and early-career professionals from engineering, computer science, data science and related fields exploring a career in AI and ML
- IT professionals, developers and data analysts upskilling in ML concepts, tools and cloud-based implementations on AWS
- Working professionals applying machine learning to real business challenges
- Educators and trainers who want industry-aligned ML knowledge for their teaching
- Career returnees and non-technical learners with a strong interest in ML who want structured exposure to AWS ML services
Skills and tools
Skills acquired Fundamentals of AI and machine learning, data preparation, cleaning and transformation for ML workflows, feature engineering and model evaluation, supervised and unsupervised learning approaches, model training, optimisation and deployment on AWS, ML workflow automation, interpreting results and improving model performance, and applying ML concepts to real industry use cases.
Tools and services covered Amazon SageMaker for building, training and deploying models at scale. AWS Deep Learning AMIs and Deep Learning Containers for TensorFlow, PyTorch and MXNet. Amazon Rekognition for image and video analysis. Amazon Comprehend for natural language processing. Amazon Polly and Amazon Transcribe for speech. AWS Glue and Amazon S3 for data preparation, storage and integration. Amazon Athena and QuickSight for querying and visualisation. AWS Lambda for serverless model execution. Amazon Machine Learning SDKs and APIs for application integration.
Certificate
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