Automation has reaped efficiencies in business and IT operations and extending it with predictive maintenance will further improve reliability. In this session, learn how to architect a predictive and preventative remediation solution for your applications and infrastructure resources. We show you how to collect performance and operational intelligence, recognise and predict patterns using AI and machine learning, and fix issues. We show you how to achieve it using AWS native solutions, Amazon SageMaker and Amazon CloudWatch.

1. S U M M I T
SYDNEY

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AIOps: Steps towards
autonomous operations
Sri (Srichakri) Nadendla
Enterprise Solutions Architect
Amazon Web Services

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Agenda
• Effective operational practices
• Enablers for autonomous operations
• Demo using Amazon Sagemaker

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Operations
School of hard knocks

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Whatever can go wrong,
will go wrong.

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Operations objectives
Keep it
safe
Keep the
lights on
Reliability
(Availability + Performance)
Security

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Ops holy grail
Prevent Correct Baseline

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Enablers to effective operations
Collection Patterns
Actions

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Foundations to observability
Alerts and
notifications
Data, tools
and patterns
Ingestion
Metrics,
events
and logs
Threat intel Budgets
Planned events

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Step 1 – Enable instrumentation
Metrics,
events
and logs

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AWS services in the context
Instrumentation

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Step 2 – Ingest and store
Data
storage
Ingestion
Metrics,
events
and logs

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AWS services in the context
Instrumentation Ingestion Storage

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Step 3 – Query and pattern mining
Data
Storage
Ingestion
Metrics,
events
and logs
Threat intel Budgets
Planned events
Analysis

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AWS services in the context
Instrumentation Ingestion Storage Analysis

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Step 4 – Alerts and Remediation
Alerts
& notifications
Data, tools
& patterns
Ingestion
Metrics,
events
and logs
Threat intel Budgets
Planned events

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AWS services in the context
Instrumentation Ingestion Storage Analysis Alerts &
Actions

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Some challenges
Metrics
galore
Dashboards
fatigue
Manual
correlation
Static
thresholds

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Responsiveness matters
Root cause
identification
Dynamic
detection
Proactive
remediation

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Path to autonomous operations
Data, tools
& patterns
Ingestion
Metrics,
events
and logs
Threat intel Budgets
Planned events
Predictive,
actionable and
automated
remediation

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Key techniques
Correlation Anomaly
detection
Forecasting

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Amazon SageMaker: Build, train, and deploy
machine learning models at scale
1
2
3

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Artificial Intelligence
amplifies the possibilities of
human-machine collaboration

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Additional considerations
Shift left
(CI/CD)
Runbook
invocation
Knowledge
assist

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GOOD intentions should have GOALs
Mean time
between
failures
Proactive
actions
# Problems
avoided
Time to detect
Time to resolve
Mean time to
recover

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What we are really trying to achieve …
Infrastructure
Support
Innovation
Infrastructure
Support
Innovation
Innovation
Support
✅

28. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Sri (Srichakri) Nadendla
nadendls@amazon.com