Practical Data Science with Amazon SageMaker
- Course Code GK0630
- Duration 1 day
- Version 1.0
Course Delivery
Jump to:
Course Delivery
This course is available in the following formats:
-
Class Connect HD
Connect to a class in HD
-
Company Event
Event at company
-
Public Classroom
Traditional Classroom Learning
-
Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopCourse Schedule
Top-
- Delivery Format: Virtual Learning
- Date: 20 September, 2024
- Location: Virtual
Target Audience
TopThis course is intended for:
- Developers
- Data Scientists
Course Objectives
TopIn this course, you will learn how to:
- Prepare a dataset for training
- Train and evaluate a Machine Learning model
- Automatically tune a Machine Learning model
- Prepare a Machine Learning model for production
- Think critically about Machine Learning model results
Course Content
TopDay One
Module 1: Introduction to Machine Learning
- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
Module 2: Introduction to Data Prep and SageMaker
- Training and Test dataset defined
- Introduction to SageMaker
- Demo: SageMaker console
- Demo: Launching a Jupyter notebook
Module 3: Problem formulation and Dataset Preparation
- Business Challenge: Customer churn
- Review Customer churn dataset
Module 4: Data Analysis and Visualization
- Demo: Loading and Visualizing your dataset
- Exercise 1: Relating features to target variables
- Exercise 2: Relationships between attributes
- Demo: Cleaning the data
Module 5: Training and Evaluating a Model
- Types of Algorithms
- XGBoost and SageMaker
- Demo 5: Training the data
- Exercise 3: Finishing the Estimator definition
- Exercise 4: Setting hyperparameters
- Exercise 5: Deploying the model
- Demo: Hyperparameter tuning with SageMaker
- Demo: Evaluating Model Performance
Module 6: Automatically Tune a Model
- Automatic hyperparameter tuning with SageMaker
- Exercises 6-9: Tuning Jobs
Module 7: Deployment / Production Readiness
- Deploying a model to an endpoint
- A/B deployment for testing
- Auto Scaling Scaling
- Demo: Configure and Test Autoscaling
- Demo: Check Hyperparameter tuning job
- Demo: AWS Autoscaling
- Exercise 10-11: Set up AWS Autoscaling
- Cost of various error types
- Demo: Binary Classification cutoff
Module 9: Amazon SageMaker Architecture and features
- Accessing Amazon SageMaker notebooks in a VPC
- Amazon SageMaker batch transforms
- Amazon SageMaker Ground Truth
- Amazon SageMaker Neo
Course Prerequisites
Top- Familiarity with Python programming language
- Basic understanding of Machine Learning
- /en-be/-/media/global-knowledge/merchandising/right-side-column/emea/gk-polaris/gk-polaris-discover-unlimited-aws-training-160x600.png https://www.globalknowledge.com/en-be/products/subscriptions/pl-discovery?utm_source=website&utm_medium=banner&utm_campaign=GK-Polaris-aws&utm_content=course-overview #000000
- GK0630
- Practical Data Science with Amazon SageMaker
- Big Data
- GK0630 - Practical Data Science with Amazon SageMaker training
- Amazon Web Services