1. Serverless ML/Analytics
Using AWS Managed Services
Cyrus M Vahid, Principal Solutions Architect
June 2017

2. Initial Batch Cycle Phase 1: Long Cycle: Batch Analytics
Decouple Data From Processing
Establish Simple ML using AML.
Amazon Athena
Team Capabilities
Kinesis Firehose
Quicksight
Kinesis Streams
Amazon S3
AML
Full Cycle: Analytics

3. Extended Batch Cycle Phase 1: Phase 2: Long Cycle: Batch Analytics
Decouple Data From Processing
Establish Simple ML using AML.
Use BI for
Phase 2:
Introduce More Sophisticated ML using EMR/Spark, as well as libraries such as scikit-learn.
Amazon Athena
Team Capabilities
Redshift
Quicksight
Amazon S3
EMR
(Yarn/Spark)
AML
Aggregator
Full Cycle: Analytics

4. Full Cycle Serverless Analysis
Long Cycle:
Batch Analytics
Phase 1:
Decouple Data From Processing
Establish Simple ML using AML.
Use BI for
Phase 2:
Introduce More Sophisticated ML using EMR/Spark, as well as libraries such as scikit-learn.
Phase 3:
Advance realtime analysis by incorporating the results from improved batch analysis.
Introduce feedback loop.
Amazon Athena
Team Capabilities
Redshift
Kinesis Firehose
Quicksight
Kinesis Streams
Amazon S3
EMR
(Yarn/Spark)
AML
Kinesis Analytics
Aggregator
Realtime Event Processor
Aggregator
Realtime Search
Feedback
Aggregator
Agg.
Cached Data
Short Cycle:
Real Time Analytics
Full Cycle: Analytics

5. Collaborative ML and Deep Learning
Long Cycle:
Batch Analytics
Phase 1:
Decouple Data From Processing
Establish Simple ML using AML.
Use BI for
Phase 2:
Introduce More Sophisticated ML using EMR/Spark, as well as libraries such as scikit-learn.
Phase 3:
Advance realtime analysis by incorporating the results from improved batch analysis.
Introduce feedback loop.
Phase 4:
Add multi data source SDK
Add deep Learning Capabilities
Amazon Athena
Team Capabilities
Redshift
Kinesis Firehose
Quicksight
Kinesis Streams
Amazon S3
EMR
(Yarn/Spark)
AML
Kinesis Analytics
Aggregator
Realtime Event Processor
Aggregator
Realtime Search
Feedback
Aggregator
Agg.
Cached Data
Short Cycle:
Real Time Analytics
Full Cycle: Analytics

6. References
Amazon S3:
Amazon Athena:
MXNet:
Amazon Kinesis:
Amazon EMR:
Amazon Redshift:
Amazon Quicksight:
Amazon Elastic Search:
Amazon Elasticache:
AWS Lambda:
Deep Learning AMI:
Amazon Elastic GPU (preview):
Amazon GPU Instances:

7. Thank You
Cyrus M Vahid