Practical Data Science with Amazon SageMaker
- Code training GK0630
- Duur 1 dag
- Versie 1.0
Andere trainingsmethoden
Methode
Deze training is in de volgende formats beschikbaar:
-
Class Connect
Verbind naar een klas in HD
-
Klassikale training
Klassikaal leren
-
Op locatie klant
Op locatie klant
-
Virtueel leren
Virtueel leren
Vraag deze training aan in een andere lesvorm.
Trainingsbeschrijving
Naar bovenData
Naar boven-
- Methode: Virtueel leren
- Datum: 20 september, 2024
- Locatie: Virtueel-en-klassikaal
- Taal: Engels
Doelgroep
Naar bovenThis course is intended for:
- Developers
- Data Scientists
Trainingsdoelstellingen
Naar bovenIn 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
Inhoud training
Naar bovenDay 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
Voorkennis
Naar boven- Familiarity with Python programming language
- Basic understanding of Machine Learning
- /nl-nl/-/media/global-knowledge/merchandising/right-side-column/emea/gk-polaris/gk-polaris-discover-unlimited-aws-training-160x600.png https://www.globalknowledge.com/nl-nl/training/online-it-training-subscriptions/gk-polaris?utm_source=website&utm_medium=banner&utm_campaign=GK-Polaris-AWS&utm_content=course-overview #000000
- #000000
- GK0630
- Practical Data Science with Amazon SageMaker
- Big Data
- GK0630 - Practical Data Science with Amazon SageMaker training
- Amazon Web Services