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

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

Video description

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.

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Introduction

Welcome To The Course 00:07:39

Introducing The Author 00:01:37

Introduction To Tools And Processes

Introducing Intelligent Application Architectures 00:11:25

Introduction To Key Tools 00:04:44

Introducing The Project Directory Structure 00:03:42

Introducing Project Workflow 00:02:07

Deploying Our First Automated Application

Designing A Data Partitioning Application 00:04:50

Creating Our ETL Pipeline Part - 1 00:03:31

Creating Our ETL Pipeline Part - 2 00:10:46

Reviewing Our Data ETL Application 00:03:35

Deploying An Automated Machine Learning Algorithm

Designing A Machine Learning Application 00:04:49

Deploying Our Machine Learning Application Part - 1 00:07:19

Deploying Our Machine Learning Application Part - 2 00:07:11

Reviewing Our Machine Learning Application 00:08:07

Integrating A Database Layer

Designing Database Layer Application 00:04:18

Loading Data Into Redshift Part - 1 00:05:31

Loading Data Into Redshift Part - 2 00:11:33

Loading Data Into RDS Part - 1 00:05:51

Loading Data Into RDS Part - 2 00:03:01

Reviewing Our Database Layer Application Integration 00:04:04

Integrating Smart Stream Ingestion

Designing A Streaming Data Ingestion Application 00:04:43

Configuring Kinesis Data Generator Part - 1 00:03:58

Configuring Kinesis Data Generator Part - 2 00:04:28

Creating Kinesis Streaming Analytics Applications Part - 1 00:05:45

Creating Kinesis Streaming Analytics Applications Part - 2 00:05:41

Creating Kinesis Streaming Analytics Applications Part - 3 00:11:44

Reviewing Kinesis Analytics 00:03:50

Creating Machine Learning Endpoints With Amazon Machine Learning

Introducing The Designing of a Amazon Machine Learning Application 00:04:17

Preparing Datasets For Amazon Machine Learning 00:07:58

Deploying Models Against Amazon Machine Learning 00:09:36

Evaluating Our Amazon Machine Learning Models 00:09:04

Calling A Real-Time Prediction Endpoint Using The Amazon ML API 00:05:25

Reviewing Amazon Machine Learning Solution 00:06:05

Wrapping Up

Wrapping Up 00:05:28

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