AWS Certified Machine Learning Study Guide. Shreyas Subramanian

Чтение книги онлайн.

СКАЧАТЬ 3.4-6. Linear models (learning rate) 8

      Subdomain 3.5: Evaluate machine learning models

Exam Objective	Chapter
3.5-1. Avoid overfitting/underfitting (detect and handle bias and variance	9
3.5-2. Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score)	9
3.5-3. Confusion matrix	9
3.5-4. Offline and online model evaluation, A/B testing	9
3.5-5. Compare models using metrics (time to train a model, quality of model, engineering costs)	9
3.5-6. Cross validation	9

      Domain 4: Machine Learning Implementation and Operations

      Subdomain 4.1: Frame Build Machine Learning Solutions for Performance, Availability, Scalability, Resiliency, and Fault Tolerance

Exam Objective	Chapter
4.1-1. AWS environment logging and monitoring	8
CloudTrail and CloudWatch	8
Build Error Monitoring	8
4.1-2. Multiple regions, Multiple AZs	14
4.1-3. Docker containers	8
4.1-4. Auto Scaling groups	10
4.1-5. Rightsizing	8, 10, 12, 15
4.1-6. Load balancing	10, 15
4.1-7. AWS best practices	12, 13, 14, 15, 16

      Subdomain 4.2: Recommend and Implement the Appropriate Machine Learning Services and Features for a Given Problem

Exam Objective	Chapter
4.2-1. ML on AWS (application services)	1
4.2-2. AWS service limits	1, 2
4.2-3. Build your own model vs. SageMaker built-in algorithms	8
4.2-4. Infrastructure: Instances types for ML and cost considerations	16

      Subdomain 4.3: Apply Basic AWS Security Practices to Machine Learning Solutions

Exam Objective	Chapter
4.3-1. IAM	2, 13
4.3-2. S3 Bucket Policies	2, 13
4.3-3. Security groups	2, 13
4.3-4. VPC	2, 13
4.3-5. Encryption/anonymization	13

      Subdomain 4.4: Deploy and Operationalize Machine Learning Solutions

Exam Objective	Chapter
4.4-1. Exposing endpoints and interacting with them	10, СКАЧАТЬ В начало < 7 8 9 10 11 12 13 > В конец