Learn How to Build Intelligent Data Applications With Amazon Web Services AWS
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.
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
01 Introduction
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00001 Welcome_To_The_Course
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00002 Introducing_The_Author
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02 Introduction To Tools And Processes
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00003 Introducing_Intelligent_Application_Architectures
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00004 Introduction_To_Key_Tools
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00005 Introducing_The_Project_Directory_Structure
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00006 Introducing_Project_Workflow
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03 Deploying Our First Automated Application
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00007 Designing_A_Data_Partitioning_Application
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00008 Creating_Our_ETL_Pipeline_Part_-_1
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00009 Creating_Our_ETL_Pipeline_Part_-_2
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00010 Reviewing_Our_Data_ETL_Application
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04 Deploying An Automated Machine Learning Algorithm
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00011 Designing_A_Machine_Learning_Application
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00012 Deploying_Our_Machine_Learning_Application_Part_-_1
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00013 Deploying_Our_Machine_Learning_Application_Part_-_2
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00014 Reviewing_Our_Machine_Learning_Application
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05 Integrating A Database Layer Designing Database Layer Application
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00015 Designing_Database_Layer_Application
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00016 Loading_Data_Into_Redshift_Part_-_1
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00017 Loading_Data_Into_Redshift_Part_-_2
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00018 Loading_Data_Into_RDS_Part_-_1
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00019 Loading_Data_Into_RDS_Part_-_2
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00020 Reviewing_Our_Database_Layer_Application_Integration
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06 Integrating Smart Stream Ingestion
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00021 Designing_A_Streaming_Data_Ingestion_Application
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00022 Configuring_Kinesis_Data_Generator_Part_-_1
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00023 Configuring_Kinesis_Data_Generator_Part_-_2
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00024 Creating_Kinesis_Streaming_Analytics_Applications_Part_-_1
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00025 Creating_Kinesis_Streaming_Analytics_Applications_Part_-_2
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00026 Creating_Kinesis_Streaming_Analytics_Applications_Part_-_3
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00027 Reviewing_Kinesis_Analytics
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07 Creating Machine Learning Endpoints With Amazon Machine Learning
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00028 Introducing_The_Designing_of_a_Amazon_Machine_Learning_Application
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00029 Preparing_Datasets_For_Amazon_Machine_Learning
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00030 Deploying_Models_Against_Amazon_Machine_Learning
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00031 Evaluating_Our_Amazon_Machine_Learning_Models
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00032 Calling_A_Real-Time_Prediction_Endpoint_Using_The_Amazon_ML_API
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00033 Reviewing_Amazon_Machine_Learning_Solution
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08 Wrapping Up
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00034 Wrapping_Up
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