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Learn How to Build Intelligent Data Applications With Amazon Web Services AWS
Learn How to Build Intelligent Data Applications With Amazon Web Services AWS
Understanding and Using AWS Products and Services: AWS Data Pipeline, Kinesis Analytics, RDS and Redshift Databases, and Amazon Machine Learning
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.
Publisher: Infinite Skills
Release Date: July 2017
Duration: 3 hours 23 minutes
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 Partitionin 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
