The Machine Learning Pipeline on AWS Certification Training Course

Candidates can achieve this certification by passing the following exam(s).

Exam Number: The Machine Learning Pipeline on AWS

The certification exam can be registered and attempted within 3 months of course/module completion at Logitrain training centre on weekdays during normal business hours (excludes public holidays)

• Select and justify the appropriate ML approach for a given business problem
• Use the ML pipeline to solve a specific business problem
• Train, evaluate, deploy, and tune an ML model in Amazon SageMaker
• Describe some of the best practices for designing scalable, cost-optimised, and secure ML pipelines in AWS
• Apply machine learning to a real-life business problem after the course is complete

• Developers
• Solutions architects
• Data engineers
• Students
• IT professionals
• People interested to learn about the ML pipeline via Amazon SageMaker

• Pre-assessment
• Overview of machine learning, including use cases, types of machine learning, and key concepts
• Overview of the ML pipeline
• Introduction to course projects and approach
• Overview of machine learning, including use cases, types of machine learning, and key concepts
• Overview of the ML pipeline
• Introduction to course projects and approach
• Introduction to Amazon SageMaker
• Amazon SageMaker and Jupyter notebooks
• Amazon SageMaker and Jupyter notebooks
• Overview of problem formulation and deciding if ML is the right solution
• Converting a business problem into an ML problem
• Amazon SageMaker Ground Truth
• Amazon SageMaker Ground Truth
• Practice problem formulation
• Formulate problems for projects
• Overview of data collection and integration, and techniques for data preprocessing and visualisation
• Practice preprocessing
• Preprocess project data and discuss project progress
• Choosing the right algorithm
• Formatting and splitting your data for training
• Loss functions and gradient descent for improving your model
• Create a training job in Amazon SageMaker
• How to evaluate classification models
• How to evaluate regression models
• Practice model training and evaluation
• Train and evaluate project models, then present findings
• Feature extraction, selection, creation, and transformation
• Hyperparameter tuning
• SageMaker hyperparameter optimisation
• Practice feature engineering and model tuning
• Apply feature engineering and model tuning to projects
• Final project presentations
• How to deploy, inference, and monitor your model on Amazon SageMaker
• Deploying ML at the edge
• Creating an Amazon SageMaker endpoint
• Post-assessment