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FRCAWS-24AWS

Amazon SageMaker Studio for Data Scientists

Duration · 3 daysVirtual + In-PersonInstructor-Led

$2,050

Course Description

This three-day instructor-led course teaches data scientists how to use Amazon SageMaker Studio to efficiently build, train, optimize, deploy, and monitor machine learning models on AWS. Participants gain hands-on experience with end-to-end machine learning workflows using SageMaker Studio, including data preparation, experimentation, feature engineering, model tuning, deployment, collaboration, and operational best practices.

The course emphasizes practical application of data science workflows using managed AWS machine learning services and interactive lab exercises.

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Audience Profile

This course is intended for:

  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • AI Researchers
  • Cloud Engineers
  • Developers working with machine learning solutions

Prerequisites

Before enrolling, you should have:

  • Basic understanding of machine learning concepts
  • Familiarity with Python programming
  • Experience working with datasets and notebooks
  • Basic understanding of AWS core services
  • Knowledge of statistics and data analysis concepts

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Navigate and use Amazon SageMaker Studio effectively
  • 2Prepare and analyze datasets for machine learning
  • 3Build and train machine learning models
  • 4Use SageMaker Studio tools for experimentation and collaboration
  • 5Perform feature engineering and hyperparameter tuning
  • 6Deploy machine learning models for inference
  • 7Monitor model performance and operational metrics
  • 8Implement scalable and repeatable ML workflows
  • 9Apply practical MLOps concepts using SageMaker

— Day-by-Day —

Course Outline

Day 1 — Foundations of Amazon SageMaker Studio

Module 1

Introduction to Machine Learning on AWS

Topics

  • Machine learning lifecycle overview
  • Introduction to Amazon SageMaker
  • Benefits of SageMaker Studio
  • AWS AI/ML ecosystem overview
  • Managed infrastructure for data science
  • Common machine learning use cases
  • Overview of SageMaker Studio components
  • AWS Services Covered
  • Amazon SageMaker Studio
  • Amazon S3
  • AWS IAM

Hands-On Lab

  • Access and configure SageMaker Studio
  • Create user profiles and notebook environments
  • Upload datasets to Amazon S3

Module 2

Navigating Amazon SageMaker Studio

Topics

  • SageMaker Studio interface walkthrough
  • Managing notebooks and kernels
  • Integrated development environments
  • Resource management and permissions
  • Collaboration features
  • Git integration basics
  • Managing compute resources
  • AWS Services Covered
  • SageMaker Studio
  • Amazon EC2
  • AWS IAM

Hands-On Lab

  • Create and manage notebook instances
  • Configure development environments
  • Integrate notebooks with Git repositories

Module 3

Data Preparation and Exploration

Topics

  • Importing and managing datasets
  • Data preprocessing fundamentals
  • Handling missing and inconsistent data
  • Exploratory Data Analysis (EDA)
  • Data visualization techniques
  • Data transformation workflows
  • Preparing datasets for model training
  • AWS Services Covered
  • SageMaker Data Wrangler
  • Amazon Athena
  • Amazon S3

Hands-On Lab

  • Perform exploratory data analysis
  • Clean and transform datasets
  • Create reusable data preparation workflows

Module 4

Feature Engineering Fundamentals

Topics

  • Feature engineering concepts
  • Feature selection strategies
  • Encoding categorical data
  • Feature scaling and normalization
  • Managing feature pipelines
  • Introduction to SageMaker Feature Store
  • AWS Services Covered
  • SageMaker Feature Store
  • SageMaker Processing

Hands-On Lab

  • Build feature engineering workflows
  • Store and manage features using Feature Store

Day 2 — Model Development and Optimization

Module 5

Building and Training Machine Learning Models

Topics

  • Supervised learning workflows
  • Training machine learning models
  • Using SageMaker built-in algorithms
  • Training with XGBoost
  • Custom model training
  • Distributed training concepts
  • Training job configuration
  • AWS Services Covered
  • SageMaker Training Jobs
  • SageMaker Processing
  • Amazon EC2

Hands-On Lab

  • Train machine learning models in SageMaker
  • Configure and run training jobs
  • Analyze training outputs and metrics

Module 6

Experimentation and Hyperparameter Optimization

Topics

  • Experiment tracking concepts
  • Managing experiments in SageMaker
  • Hyperparameter tuning fundamentals
  • Automated model tuning
  • Comparing training runs
  • Evaluating model performance
  • AWS Services Covered
  • SageMaker Experiments
  • SageMaker Automatic Model Tuning

Hands-On Lab

  • Track machine learning experiments
  • Perform automated hyperparameter tuning
  • Compare multiple model versions

Module 7

Model Evaluation and Validation

Topics

  • Model evaluation methodologies
  • Classification and regression metrics
  • Accuracy, precision, recall, and F1-score
  • Cross-validation techniques
  • Detecting overfitting and underfitting
  • Model explainability basics
  • AWS Services Covered
  • SageMaker Clarify
  • SageMaker Processing

Hands-On Lab

  • Evaluate trained models
  • Generate performance metrics
  • Analyze explainability reports

Module 8

Collaborative Data Science Workflows

Topics

  • Team collaboration in SageMaker Studio
  • Version control best practices
  • Sharing notebooks and experiments
  • Managing artifacts and datasets
  • Reproducible workflows
  • Governance considerations

Hands-On Lab

  • Share notebooks and experiments
  • Collaborate using shared SageMaker resources

Day 3 — Deployment, Monitoring, and Operational ML

Module 9

Deploying Machine Learning Models

Topics

  • Real-time inference concepts
  • Batch inference concepts
  • Hosting models with SageMaker endpoints
  • Multi-model endpoints
  • Serverless inference
  • Deployment best practices
  • Scaling inference workloads
  • AWS Services Covered
  • SageMaker Endpoints
  • SageMaker Batch Transform
  • SageMaker Serverless Inference
  • AWS Lambda

Hands-On Lab

  • Deploy machine learning models
  • Test real-time inference endpoints
  • Perform batch prediction jobs

Module 10

Monitoring and Managing ML Workloads

Topics

  • Monitoring ML systems
  • Logging and operational metrics
  • Detecting data and model drift
  • Endpoint monitoring
  • Alerting and notifications
  • Cost optimization for ML workloads
  • AWS Services Covered
  • Amazon CloudWatch
  • SageMaker Model Monitor
  • AWS CloudTrail

Hands-On Lab

  • Configure model monitoring
  • Create dashboards and alerts
  • Analyze endpoint performance metrics

Module 11

Introduction to MLOps with SageMaker

Topics

  • MLOps principles and workflows
  • CI/CD concepts for machine learning
  • Automating ML pipelines
  • Managing model versions
  • Model governance and approval workflows
  • Repeatable and scalable ML operations
  • AWS Services Covered
  • SageMaker Pipelines
  • SageMaker Model Registry
  • AWS CodePipeline
  • AWS CodeBuild

Hands-On Lab

  • Create automated ML pipelines
  • Register and approve machine learning models

Module 12

End-to-End Capstone Project

Topics

  • Building production-ready ML workflows
  • Integrating data preparation, training, and deployment
  • Operationalizing machine learning solutions
  • Troubleshooting ML workflows
  • Best practices for enterprise ML
  • Capstone Hands-On Lab
  • Participants complete a full machine learning project that includes:
  • Data ingestion and preparation
  • Exploratory data analysis
  • Feature engineering
  • Model training and tuning
  • Model evaluation
  • Model deployment
  • Monitoring and operational management
  • Course Wrap-Up
  • Final Review
  • Review of key concepts and workflows
  • Common implementation patterns
  • AWS best practices for data science and ML
  • Skills Validation
  • Hands-on lab reviews
  • Interactive discussions
  • Capstone presentation and review
  • Next Steps
  • Advanced SageMaker and MLOps training
  • AWS machine learning certification pathways
  • Production ML implementation guidance
  • AWS Services Covered
  • Amazon SageMaker Studio
  • SageMaker Data Wrangler
  • SageMaker Feature Store
  • SageMaker Training Jobs
  • SageMaker Processing
  • SageMaker Experiments
  • SageMaker Automatic Model Tuning
  • SageMaker Clarify
  • SageMaker Endpoints
  • SageMaker Batch Transform
  • SageMaker Serverless Inference
  • SageMaker Model Monitor
  • SageMaker Pipelines
  • SageMaker Model Registry
  • Amazon S3
  • Amazon Athena
  • Amazon EC2
  • AWS Lambda
  • AWS IAM
  • AWS CodePipeline
  • AWS CodeBuild
  • Amazon CloudWatch
  • AWS CloudTrail

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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