Learn How to Build Intelligent Data Applications With Amazon Web Services (AWS)

Learn How to Build Intelligent Data Applications With Amazon Web Services (AWS) image

Understanding and Using AWS Products and Services: AWS Data Pipeline, Kinesis Analytics, RDS and Redshift Databases, and Amazon Machine Learning

Publisher: Infinite Skills

Release Date: July 2017

Duration: 3 hours 23 minutes

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This course shows you how to use a range of AWS services to create intelligent end-to-end applications that incorporate ingestion, storage, preprocessing, machine learning (ML), and connectivity to an application client or server. The course is designed for data scientists looking for clear instruction on how to deploy locally developed ML applications to the AWS platform, and for developers who want to add machine learning capabilities to their applications using AWS services. Prerequisites include: Basic awareness of Amazon Simple Storage Service (S3), Elastic Compute Cloud (EC2), and Amazon Elastic MapReduce; as well as some knowledge of ML concepts like classification and regression analysis, model types, training and performance measures; and a general understanding of Python.

  • Understand how to use Amazon Web Service's best-in-class streaming analytics and ML tools
  • Learn about Amazon data pipelines: A very lightweight way to deploy an ML algorithm
  • Explore Redshift and RDS: Databases that stage input data or store model outputs
  • Discover Kinesis: A streaming data ingestion service that performs streaming analytical functions
  • Learn to apply streaming and batch analytical processing to prepare datasets for ML algorithms
  • Gain experience building ML models using Amazon Machine Learning and calling them using Python

John Hearty is a data scientist with Relic Entertainment who specializes in using Amazon Web Services to develop data infrastructure and analytics solutions. He is the author or co-author of three highly regarded books on machine learning (e.g., Packt Publishing's "Advanced Machine Learning with Python") and holds a Master's degree in Computer Science from Liverpool John Moores University.

Table of Contents

Chapter: Introduction

Welcome To The Course

07m 39s

Introducing The Author

01m 36s

Chapter: Introduction To Tools And Processes

Introducing Intelligent Application Architectures

11m 25s

Introduction To Key Tools

04m 44s

Introducing The Project Directory Structure

03m 42s

Introducing Project Workflow

02m 7s

Chapter: Deploying Our First Automated Application

Designing A Data Partitioning Application

04m 50s

Creating Our ETL Pipeline Part - 1

03m 30s

Creating Our ETL Pipeline Part - 2

10m 46s

Reviewing Our Data ETL Application

03m 34s

Chapter: Deploying An Automated Machine Learning Algorithm

Designing A Machine Learning Application

04m 48s

Deploying Our Machine Learning Application Part - 1

07m 18s

Deploying Our Machine Learning Application Part - 2

07m 10s

Reviewing Our Machine Learning Application

08m 6s

Chapter: Integrating A Database Layer

Designing Database Layer Application

04m 17s

Loading Data Into Redshift Part - 1

05m 30s

Loading Data Into Redshift Part - 2

11m 33s

Loading Data Into RDS Part - 1

05m 50s

Loading Data Into RDS Part - 2

03m 0s

Reviewing Our Database Layer Application Integration

04m 4s

Chapter: Integrating Smart Stream Ingestion

Designing A Streaming Data Ingestion Application

04m 43s

Configuring Kinesis Data Generator Part - 1

03m 57s

Configuring Kinesis Data Generator Part - 2

04m 28s

Creating Kinesis Streaming Analytics Applications Part - 1

05m 44s

Creating Kinesis Streaming Analytics Applications Part - 2

05m 40s

Creating Kinesis Streaming Analytics Applications Part - 3

11m 44s

Reviewing Kinesis Analytics

03m 50s

Chapter: Creating Machine Learning Endpoints With Amazon Machine Learning

Introducing The Designing of a Amazon Machine Learning Application

04m 16s

Preparing Datasets For Amazon Machine Learning

07m 58s

Deploying Models Against Amazon Machine Learning

09m 36s

Evaluating Our Amazon Machine Learning Models

09m 3s

Calling A Real-Time Prediction Endpoint Using The Amazon ML API

05m 25s

Reviewing Amazon Machine Learning Solution

06m 5s

Chapter: Wrapping Up

Wrapping Up

05m 28s

  • Publication date: 01.08.2017
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