Practical Data Science with Amazon SageMaker
The Practical Data Science with Amazon SageMaker (MLPDSS) course is designed for participants who want to learn how to use Amazon SageMaker to build, train, and deploy machine learning models. During the course, participants will gain hands-on skills in building datasets, training machine learning models, evaluating model performance, and deploying trained models on AWS. Additionally, the course provides an overview of AWS AI Services and AWS serverless architecture. This course helps prepare for the AWS Certified Machine Learning – Specialty certification .
Course Objectives
The following is a summary of the main objectives of the Practical Data Science with Amazon SageMaker (MLPDSS) course :
- Learn how to use Amazon SageMaker to build, train, and deploy machine learning models.
- Gain hands-on skills in building datasets and training machine learning models.
- Evaluate the performance of machine learning models.
- Deploy trained models to AWS.
- Get an overview of AWS AI Services and AWS serverless architecture.
- Implement best practices for model optimization and hyperparameter tuning using SageMaker.
- Manage model lifecycle, including versioning and monitoring, within SageMaker.
- Integrate SageMaker with other AWS services for end-to-end machine learning workflows and data processing.
Course Certification
This course helps you prepare to take the:
AWS Certified Machine Learning – Specialty Exam ;
Course Outline
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
- Demonstration: SageMaker console
- Demonstration: 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
- Demonstration: Loading and visualizing your dataset
- Exercise 1: Relating features to target variables
- Exercise 2: Relationships between attributes
- Demonstration: Cleaning the data
Module 5: Training and evaluating a model
- Types of algorithms
- XGBoost and SageMaker
- Demonstration: Training the data
- Exercise 3: Finishing the estimator definition
- Exercise 4: Setting hyper parameters
- Exercise 5: Deploying the model
- Demonstration: hyper parameter tuning with SageMaker
- Demonstration: Evaluating model performance
Module 6: Automatically tune a model
- Automatic hyper parameter 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
- Demonstration: Configure and test auto scaling
- Demonstration: Check hyper parameter tuning job
- Demonstration: AWS Auto Scaling
- Exercise 10-11: Set up AWS Auto Scaling
Module 8: Relative cost of errors
- 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 Mode
Instructor-Led Remote Live Classroom Training;
Trainers
Trainers are Amazon AWS accredited instructors and certified in other IT technologies, with years of practical experience in the sector and in training.
Lab Topology
For all types of delivery, the participant can access the equipment and actual systems in our laboratories or directly in international data centers remotely, 24/7. Each participant has access to implement various configurations, Thus immediately applying the theory learned. Below are some scenarios drawn from laboratory activities.
Course Details
Course Prerequisites
Participation in the following courses is recommended:
- AWS Technical Essentials
- DevOps Engineering on AWS
- Machine Learning Terminology and Process
- The Elements of Data Science
Course Duration
Intensive duration 1 days;
Course Frequency
Course Duration: 1 days (9.00 to 17.00) - Ask for other types of attendance.
Course Date
- Practical Data Science with Amazon SageMaker (Intensive Formula) – On Request – 9:00 – 17:00
Steps to Enroll
Registration takes place by asking to be contacted from the following link, or by contacting the office at the international number +355 45 301 313 or by sending a request to the email info@hadartraining.com
