Practical Machine Learning with Aws: Process, Build, Deploy, and Productionize Your Models Using Aws (Paperback)

Chapter 1: AWS Concepts and Technologies

Introduction to services like S3, EC2, Identity Access Management, Roles, Load Balancer, Cloud Formation, etc.

Chapter 2: AWS Billing and Pricing

Chapter 3: AWS Cloud Security

Description about AWS compliance and artifacts, AWS Shield, Cloudwatch, Cloud Trail, etc.

Part-II ? Machine Learning in AWS (300 Pages)

Chapter 4: Data Collection and Preparation

Concepts include AWS data stores, migration and helper tools. It also includes pre-processing concepts like encoding, feature engineering, missing values removal, etc.

Chapter 5: Data Modelling and Algorithms

In this section, we will talk about all the algorithms that AWS supports, including regression, clustering, classification, image, and text analytics, etc. We will then look at Sagemaker service and how to make models using it.

Chapter 6: Data Analysis and Visualization

This chapter talks about the relationship between variables, data distributions, the composition of data, etc.

Chapter 7: Model Evaluation and Optimization

This chapter talks about the monitoring of training jobs, evaluating the model accuracy, and fine-tuning models.

Chapter 8: Implementation and Operation

In this chapter, we’ll look at the deployment of models, security, and monitoring.

Chapter 9: Building a Machine Learning Workflow

In this chapter, we’ll look at the machine learning workflow in AWS .

Part-IV ? Projects (100 Pages)

Chapter 10: Project ? Building skills with Alexa

Chapter 12: Project ? Modelling and deployment using XGBoost in Sagemaker

Chapter 13: Text classification using Amazon comprehend and textract

Chapter 14: Building a complete project pipeline

Himanshu Singh (지은이)    정보 더보기

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