Amazon Sagemakaer Essesntials

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Overview

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The **Amazon SageMaker Essentials** course is designed to help you build a strong foundation in Amazon SageMaker and the end-to-end machine learning lifecycle on AWS. You will learn how to prepare data, build and train machine learning models, optimize performance, deploy models, and monitor machine learning workloads using Amazon SageMaker. This training course introduces Amazon SageMaker, AWS's fully managed machine learning service. You will explore key SageMaker capabilities for data preparation, feature engineering, model training, hyperparameter tuning, deployment, monitoring, and MLOps, while learning best practices for building scalable machine learning solutions. This course contains **5+ hours** of training videos with **35 lectures** covering Amazon SageMaker fundamentals and machine learning workflows. The lectures are organized into **5 modules**, with each module divided into lessons. The course also includes **Assessments** and **Graded Questions** at the end of every module to reinforce learning. Module 1: Amazon SageMaker Foundations Module 2:Machine Learning Data Preparation Module 3:Building and Training Machine Learning Models Module 4: Model Optimization and Performance Tuning Module 5: Model Deployment, Monitoring, and Lifecycle Management

Syllabus

Amazon SageMaker Foundations

In this section, you'll build a strong foundation in Amazon SageMaker and learn how to use its core capabilities to support the end-to-end machine learning lifecycle on AWS. You'll begin by exploring the fundamentals of Amazon SageMaker and setting up a development environment, gaining an understanding of how SageMaker simplifies building, training, and deploying machine learning models. As you progress, you'll discover how Amazon SageMaker Data Wrangler streamlines data preparation and transformation for machine learning workflows. You'll also learn how Amazon SageMaker Feature Store enables you to create, store, and manage reusable machine learning features, improving consistency and collaboration across ML projects. The section further introduces Amazon SageMaker Model Monitor, which helps continuously monitor deployed models for data quality and model performance, ensuring reliable predictions in production environments. You'll also explore Amazon SageMaker JumpStart and learn how to accelerate machine learning development using pre-trained models, solution templates, and built-in algorithms to quickly build AI and ML applications. By the end of this section, you'll have a solid understanding of Amazon SageMaker's core services, including data preparation, feature management, model monitoring, and JumpStart capabilities, enabling you to efficiently develop, manage, and deploy machine learning solutions on AWS.

Machine Learning Data Preparation

In this section, you'll learn how to prepare high-quality datasets for machine learning by applying essential data preprocessing and feature engineering techniques in Amazon SageMaker. You'll begin by exploring data cleaning and transformation methods that help improve data quality, handle inconsistencies, and prepare datasets for effective model training. As you progress, you'll dive into feature engineering techniques to create meaningful features that enhance model performance. You'll also learn common encoding methods, including One-Hot Encoding, Label Encoding, and Tokenization, and understand how these techniques convert categorical and text data into machine learning-ready formats. The section further explores responsible data preparation by addressing bias in datasets and learning strategies to identify and reduce its impact on machine learning models. You'll also gain hands-on experience with Amazon SageMaker Ground Truth for creating high-quality labeled datasets and Amazon SageMaker Clarify for detecting bias and explaining model predictions, helping you build fairer and more transparent AI solutions. By the end of this section, you'll have a solid understanding of data preparation, feature engineering, encoding techniques, bias mitigation, and SageMaker tools for data labeling and model explainability, enabling you to create reliable, high-quality datasets for machine learning workflows.

Building and Training Machine Learning Models

In this section, you'll learn how to build and train machine learning models using Amazon SageMaker. You'll begin by exploring SageMaker built-in algorithms and understanding how they simplify the model development process by providing optimized algorithms for a wide range of machine learning tasks. As you progress, you'll examine popular supervised machine learning algorithms, including Linear Learner, XGBoost, LightGBM, and K-Nearest Neighbors (k-NN). You'll learn the strengths and common use cases of each algorithm, enabling you to select the most appropriate model based on your data and business requirements. The section also introduces key model training concepts such as epochs, batch size, and training steps, helping you understand how these parameters influence model learning and performance. Through guided demonstrations, you'll gain hands-on experience training machine learning models in Amazon SageMaker and learn the importance of splitting datasets into training and testing sets to accurately evaluate model performance. By the end of this section, you'll have a solid understanding of Amazon SageMaker's model training capabilities, built-in algorithms, training configurations, and dataset preparation techniques, enabling you to build and evaluate machine learning models with confidence.

Model Optimization and Performance Tuning

In this section, you'll learn how to optimize machine learning models and improve their performance using Amazon SageMaker. You'll begin by exploring different inference options, including real-time and batch inference, and understand when to use each approach based on application requirements and deployment scenarios. As you progress, you'll discover Amazon SageMaker tools that simplify model optimization and experimentation. You'll learn how to use SageMaker Model Debugger to identify training issues, SageMaker Experiments to track and compare model training runs, and cross-validation techniques to evaluate model performance more effectively. The section also focuses on improving model accuracy through hyperparameter tuning and Amazon SageMaker Automatic Model Tuning. You'll learn how to identify and address common machine learning challenges such as overfitting and underfitting, and explore model ensembling techniques that combine multiple models to enhance prediction accuracy and overall model performance. By the end of this section, you'll have a solid understanding of model optimization strategies, performance tuning techniques, and Amazon SageMaker tools that help build accurate, reliable, and production-ready machine learning models.

Model Deployment, Monitoring, and Lifecycle Management

In this section, you'll learn how to deploy, monitor, and manage machine learning models using Amazon SageMaker. You'll begin by exploring compute instance options, including CPU and GPU instances, and understand how to select the appropriate infrastructure based on model complexity, performance requirements, and cost considerations. As you progress, you'll discover the different Amazon SageMaker endpoint types, including Serverless, Asynchronous, and Multi-Model Endpoints, and learn when to use each deployment option. You'll also gain hands-on experience deploying models and exposing SageMaker endpoints to serve predictions for real-world machine learning applications. The section further introduces workflow automation and lifecycle management using Apache Airflow and SageMaker Pipelines. You'll explore CI/CD principles for machine learning workflows, enabling you to automate model training, testing, deployment, and version management for scalable MLOps implementations. Finally, you'll learn how to monitor deployed models using Amazon SageMaker Model Monitor to detect data and prediction anomalies and use SageMaker Inference Recommender to identify optimal deployment configurations for improved performance and cost efficiency. By the end of this section, you'll have a solid understanding of model deployment strategies, workflow orchestration, monitoring techniques, and lifecycle management practices required to deploy, maintain, and optimize production-ready machine learning models on Amazon SageMaker.