課程資料
時間表
- 2026年10月14日(週三) - 2026年10月16日(週五) 上午 9:30 - 下午 5:00
- 2026年12月9日(週三) - 2026年12月11日(週五) 上午 9:30 - 下午 5:00
報名日期
2026年6月23日(週二) - 2026年12月8日(週二)
價錢
HKD 14,400
(Early Bird HK14400
Standard HK18000)
(Early Bird HK14400
Standard HK18000)
課程級別
學習模式
修業期
3 日
教學語言
廣東話
地點
2/F, Centre Point, 181 Gloucester Road, Wanchai, HK
證書
- 可獲發
- *證書的發放與分配，依課程提供者的政策及安排而定。
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課程簡介
Course description
Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML
professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate,
and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.
Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such
as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.
- • Course level: Intermediate
- • Duration: 3 days
Activities
- This course includes presentations, hands-on labs, demonstrations, and group exercises.
Course objectives
In this course, you will:
• Explain ML fundamentals and its applications in the AWS Cloud.
• Process, transform, and engineer data for ML tasks by using AWS services.
• Select appropriate ML algorithms and modeling approaches based on problem requirements and
model interpretability.
• Design and implement scalable ML pipelines by using AWS services for model training, deployment,
and orchestration.
• Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
• Discuss appropriate security measures for ML resources on AWS.
• Implement monitoring strategies for deployed ML models, including techniques for detecting data
drift.
課程內容
Day 1
Module 0: Course Introduction
Module 1: Introduction to Machine Learning (ML) on AWS
• Topic A: Introduction to ML
• Topic B: Amazon SageMaker AI
• Topic C: Responsible ML
Module 2: Analyzing Machine Learning (ML) Challenges
• Topic A: Evaluating ML business challenges
• Topic B: ML training approaches Topic C: ML training algorithms
Module 3: Data Processing for Machine Learning (ML)
• Topic A: Data preparation and types
• Topic B: Exploratory data analysis
• Topic C: AWS storage options and choosing storage
Module 4: Data Transformation and Feature Engineering
• Topic A: Handling incorrect, duplicated, and missing data
• Topic B: Feature engineering concepts
• Topic C: Feature selection techniques
• Topic D: AWS data transformation services
• Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
• Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK
Day 2
Module 5: Choosing a Modeling Approach
• Topic A: Amazon SageMaker AI built-in algorithms
• Topic B: Selecting built-in training algorithms
• Topic C: Amazon SageMaker Autopilot
• Topic D: Model selection considerations
• Topic E: ML cost considerations
Module 6: Training Machine Learning (ML) Models
• Topic A: Model training concepts
• Topic B: Training models in Amazon SageMaker AI
• Lab 3: Training a model with Amazon SageMaker AI
Module 7: Evaluating and Tuning Machine Learning (ML) models
• Topic A: Evaluating model performance• Topic B: Techniques to reduce training time
• Topic C: Hyperparameter tuning techniques
• Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI
Module 8: Model Deployment Strategies
• Topic A: Deployment considerations and target options
• Topic B: Deployment strategies
• Topic C: Choosing a model inference strategy
• Topic D: Container and instance types for inference
• Lab 5: Shifting Traffic A/B
