AWS: Model Training , Optimization & Deployment

本课程是 Exam Prep MLA-C01: AWS Machine Learning Engineer Assocaite 专项课程的一部分

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3个模块

深入了解一个主题并学习基础知识。

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8 小时完成

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自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Explore built-in algorithms in Amazon SageMaker such as Linear Learner, XGBoost, LightGBM, and k-NN for ML model development.
Configure key training parameters like epochs, batch size, and steps to train and evaluate ML models effectively.
Compare real-time and batch inference approaches to determine the best strategy for model deployment.

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本课程是 Exam Prep MLA-C01: AWS Machine Learning Engineer Assocaite 专项课程专项课程的一部分

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该课程共有3个模块

AWS: Model Training, Optimization & Deployment is the third course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course is designed to equip learners with the skills to train, optimize, and deploy machine learning models efficiently using AWS services.

Learners begin by exploring popular algorithms such as Linear Learner, XGBoost, LightGBM, and k-Nearest Neighbors (k-NN), and understand their use cases in classification and regression tasks.You’ll then dive into the model training process, learning how to configure key parameters like epochs, batch size, and steps for optimized performance. Then the learners will begin by exploring SageMaker Model Debugger and SageMaker Experiments, which help monitor training jobs and compare experiment results efficiently.You’ll then dive into cross-validation techniques and learn how to apply hyperparameter tuning using both random search and Bayesian optimization methods to improve model accuracy. Finally by exploring compute options such as Amazon ECS, Amazon EKS, and AWS Lambda, followed by infrastructure management with AWS CloudFormation.You’ll learn how to implement auto scaling policies for ML workloads and choose the right SageMaker compute instance types (CPU vs. GPU) for different deployment scenarios. This course is divided into three comprehensive modules, each containing targeted lessons and practical demonstrations. Learners will benefit from approximately 3.5 to 4 hours of expert-led video content, featuring real-world use cases and hands-on walkthroughs using AWS tools. Every module includes Graded and Ungraded Quizzes to assess conceptual understanding and application. Module 1: Model Training, Algorithms & Inference Techniques Module 2: Model Optimization, Evaluation & Tuning with SageMaker Module 3: Scalable Infrastructure & Automated ML Deployment on AWS By the end of this course, learners will be able to: Compare real-time and batch inference approaches to determine the best strategy for model deployment. Apply model optimization techniques such as hyperparameter tuning Understand and select appropriate inference strategies for deployment Explore AWS compute and orchestration services like ECS, EKS, Lambda, and CloudFormation for ML deployment. This course is ideal for ML practitioners, data scientists, and cloud developers who are looking to scale their ML workflows and gain hands-on experience with advanced features of Amazon SageMaker. It is also designed for learners preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam, focusing on the model training and deployment aspects of the certification.

Welcome to Week 1 of the AWS: Model Training, Optimization & Deployment course. This week, you’ll focus on building machine learning models using Amazon SageMaker’s built-in algorithms. We’ll begin by exploring popular algorithms such as Linear Learner, XGBoost, LightGBM, and k-Nearest Neighbors (k-NN), and understand their use cases in classification and regression tasks. You’ll then dive into the model training process, learning how to configure key parameters like epochs, batch size, and steps for optimized performance. Through hands-on demos, you’ll practice training models, splitting datasets into train-test sets, and preparing them for evaluation. We’ll conclude the week by comparing real-time vs. batch inference, helping you understand how to choose the appropriate inference strategy based on your workload and deployment needs.

涵盖的内容

10个视频2篇阅读材料2个作业1个讨论话题

10个视频总计59分钟

SageMaker built-in algorithms5分钟
Linear Learner in SageMaker4分钟
XGBoost in SageMaker5分钟
LightGBM in SageMaker5分钟
K-Nearest Neighbors (k-NN) Algorithm5分钟
Model Training (Epoch, Batch Size, Steps)5分钟
LightGBM Algorithm5分钟
Train Machine Learning models - Demo11分钟
Train Machine Learning models - Split the Dataset - Train - Test6分钟
Real-Time vs. Batch Inference4分钟

2篇阅读材料总计60分钟

Welcome to the Course30分钟
Overview of Model Training, Algorithms & Inference Techniques30分钟

2个作业总计60分钟

Building ML Models with SageMaker Algorithms & Inference - Knowledge Check30分钟
Model Training, Algorithms & Inference Techniques - Assessment30分钟

1个讨论话题总计10分钟

Meet and Greet10分钟

Welcome to Week 2 of the AWS: Model Training, Optimization & Deployment course. This week, you'll focus on optimizing and managing machine learning models to ensure high performance and reliability in production environments. We'll begin by exploring SageMaker Model Debugger and SageMaker Experiments, which help monitor training jobs and compare experiment results efficiently. You’ll then dive into cross-validation techniques and learn how to apply hyperparameter tuning using both random search and Bayesian optimization methods to improve model accuracy. We’ll also cover model ensembling techniques, such as stacking and boosting, to combine multiple models for better predictive power. By the end of the week, you’ll learn how to manage model versions using SageMaker Model Registry, apply automatic model tuning, and implement strategies to detect and prevent overfitting or underfitting for building robust ML solutions.

涵盖的内容

9个视频1篇阅读材料2个作业

9个视频总计43分钟

SageMaker Model Debugger6分钟
SageMaker Experiments4分钟
Cross Validation techniques4分钟
Hyperparameter Tuning (Random Search, Bayesian Optimization)5分钟
Model Ensembling Techniques (Stacking, Boosting)5分钟
Managing Model Versions with SageMaker Model Registry4分钟
Sagemaker Automatic Model Tuning4分钟
Methods to identify model overfitting and underfitting4分钟
Preventing Overfitting & Underfitting5分钟

1篇阅读材料总计30分钟

Overview of Model Optimization, Evaluation & Tuning with SageMaker30分钟

2个作业总计60分钟

Advanced Model Tuning, Debugging & Performance Optimization - Knowledge Check30分钟
Model Optimization, Evaluation & Tuning with SageMaker - Assessment30分钟

Welcome to Week 3 of the AWS: Model Training, Optimization & Deployment course. This week, you’ll focus on deploying machine learning models efficiently using scalable infrastructure and automation tools on AWS. We’ll begin by exploring compute options such as Amazon ECS, Amazon EKS, and AWS Lambda, followed by infrastructure management with AWS CloudFormation. You’ll learn how to implement auto scaling policies for ML workloads and choose the right SageMaker compute instance types (CPU vs. GPU) for different deployment scenarios. We'll also cover SageMaker Endpoint types, including serverless, asynchronous, and multi-model endpoints, to help you deliver predictions at scale. Finally, you’ll dive into workflow orchestration using Apache Airflow and SageMaker Pipelines, and understand the role of CI/CD principles in automating and streamlining ML deployments.

涵盖的内容

9个视频3篇阅读材料2个作业

9个视频总计60分钟

Amazon ECS5分钟
Amazon EKS9分钟
AWS Lambda10分钟
AWS CloudFormation7分钟
AWS Auto Scaling Policies for ML Workloads5分钟
SageMaker compute instances (Compute Resource Selection (CPU vs. GPU)4分钟
SageMaker Endpoint Types (Serverless, Asynchronous, Multi-Model)4分钟
Workflow Orchestrator: Apache Airflow and SageMaker Pipelines8分钟
CI/CD Principles in ML Workflows4分钟

3篇阅读材料总计90分钟

Overview of Scalable Infrastructure & Automated ML Deployment on AWS30分钟
Course Conclusion30分钟
What's Next ?30分钟

2个作业总计60分钟

ML Deployment & Automation with AWS Infrastructure - Knowledge Check30分钟
Scalable Infrastructure & Automated ML Deployment on AWS - Assessment30分钟

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To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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