AWS Certified Machine Learning: Model Training & Evaluation
Amazon Web Services
| Intermediate
- 12 videos | 30m 54s
- Includes Assessment
- Earns a Badge
Training a machine learning (ML) model is the first step of many when developing ML applications that enable businesses to discover new trends within broad and diverse data sets. Use this course to learn more about SageMaker's built-in algorithm and perform model training, evaluation, monitoring, tuning, and deployment using Amazon Elastic Compute Cloud (EC2) instances. Begin by examining factorization machines and the selection of EC2 instances. Next, you'll discover how to perform model training, evaluation, and deployment. You'll wrap up the course by exploring the steps involved in tuning and testing ML models. After you're done with this course, you'll have the skills and knowledge to successfully train and evaluate a model, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Describe how factorization machines work and specify why they are powerful tools for recommender systems
List and describe ec2 instances that can be used with sagemaker
Work with training recommender system on amazon reviews dataset using python and sagemaker
Demonstrate how to reduce cost while training machine learning algorithms using spot instances
Evaluate a trained machine learning algorithm -
Deploy a machine learning model using api endpoints
Monitor api usage in real-time
Work with feature engineering and machine learning experimentations using python and sagemaker
Demonstrate how to run hyperparameter tuning jobs with sagemaker using python and amazon reviews dataset
Conduct a/b testing for models trained on amazon reviews dataset using production variants
Summarize the key concepts covered in this course
IN THIS COURSE
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53s
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2m 54sAfter completing this video, you will be able to describe how factorization machines work and why they are powerful tools for recommender systems. FREE ACCESS
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3. EC2 Instances3m 15sIn this video, you will learn how to list and describe EC2 instances that can be used with SageMaker. FREE ACCESS
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4. Training with EC2 Instances3m 14sUpon completion of this video, you will be able to work with a training recommender system on the Amazon Reviews dataset using Python and SageMaker. FREE ACCESS
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5. Training with EC2 Spot Instances1m 24sDuring this video, you will learn how to reduce cost while training machine learning algorithms using Spot instances. FREE ACCESS
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6. Evaluating Machine Learning Models1m 1sTo evaluate a trained machine learning algorithm, you can use a variety of methods, including accuracy, precision, recall, and F1 score. FREE ACCESS
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7. Deploying Machine Learning Models6m 19sLearn how to deploy a machine learning model using API endpoints. FREE ACCESS
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8. Monitoring Machine Learning Models1m 55sIn this video, you will learn how to monitor API usage in real time. FREE ACCESS
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9. Working with Feature Engineering3m 38sDuring this video, you will discover how to work with feature engineering and machine learning experimentations using Python and SageMaker. FREE ACCESS
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10. Performing Hyperparameter Tuning3m 19sIn this video, discover how to apply hyperparameter tuning jobs with SageMaker using Python and the Amazon Reviews dataset. FREE ACCESS
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11. Using Production Variants2m 28sDiscover how to conduct A/B testing for models trained on the Amazon Reviews dataset using production variants. FREE ACCESS
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12. Course Summary34sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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