DevOpsDaysRiga 2018: Antonio Pigna - Put the brAIn into your DevOps workflow

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Artificial Intelligence (AI) becomes more and more an element of differentiation to manage the increasing complexity of information. What steps you should do to get your infrastructure AI ready? The presentation will focus on the opportunities of using AI to make DevOps ecosystem efficient. Presentation is going to be lead by Antonio Pigna, Automation Architect @ Accenture, based in Naples, Italy.

Technology

Copyright © 2017 Accenture All rights reserved.
DevOpsDays Riga 2018
2
Put the brAIn into your
DevOps workflow
Sept 2018Antonio Pigna
Accenture - Italy

Copyright © 2017 Accenture All rights reserved.
AI is going to be everywhere
2
It’s a good idea AI for devops?
Really there are benefits?
robotic
Speech / NLP
vision
Machine learning

Copyright © 2017 Accenture All rights reserved.
The status of AI
2
Challenge for AI
• Computer power (ML)
• People power
• High specialization
• Write code => think the model
AI

Copyright © 2017 Accenture All rights reserved.
The status of DevOps
2
DevOps become more complex
• More and more pipelines
• Complex CI/CD
• More provider involved (data centers, API
srvc, net srvc)
• More layered (cloud, containers, on
premise)
• More QA gateway (code, security,
compliance, social consensus)
• Monitoring
AI

Copyright © 2017 Accenture All rights reserved.
More complexity means more risks
2
• More «items» are less agile to controls
• Scalability
• Quality
• Application performance
• Pipeline performance (maintenance, mttr)
• Visibility/control of the process

Copyright © 2017 Accenture All rights reserved.
Devops generates data
2
build
• Issues, metrics
CI
• CI metrics
CD
• Quality gates metrics
Prod/post prod
• Logfiles
• Performance metrics

Copyright © 2017 Accenture All rights reserved.
DevOps generates data over stages
2

Copyright © 2017 Accenture All rights reserved.
DevOps generates data over environments
2

Copyright © 2017 Accenture All rights reserved.
Data needs to be monitored and understood
2
Identify bad trends
Diagnose failures
Diagnose performance issues
Application behavior monitoring

Copyright © 2017 Accenture All rights reserved.
Machine learning
2
How ML traits data
• Clustering
• classification
• Regression

Copyright © 2017 Accenture All rights reserved.
Which data?
2
Devops has data that can be used to train a ML
Health of an application
• log patterns
• Resource usage
Trends
• Increase of QA gateway
• Decrease of performance
Failures
• Pattern of failures

Copyright © 2017 Accenture All rights reserved.
How ML helps DevOps
2
Decision Complexity
• Why application lacks?
• Are resources enough?
• and scalability?
Massive data
• The whole echosystem generates too much
datapoint to allows a simple trend discovery

Copyright © 2017 Accenture All rights reserved.
Man VS Machine
2
Man analyzes better
Machine analyzes even more data
Way of thinking
Limitation of domain
 Man needs huge amount of time to read data.
 Man commits errors
 Machine needs to be trained
 Quality of data matters
 No single answer (close enough)
 Debugging ML???

Copyright © 2017 Accenture All rights reserved.
Man VS Machine
2
Remember that ML is an assistant
Human SME could help ML to perform better

Copyright © 2017 Accenture All rights reserved.
Adaptive systems
2
Prediction of future events
• deterioration of performance
• Availability
• Time to release
• Resource usage

Copyright © 2017 Accenture All rights reserved.
Benefits
2
Better decision making
Easier access to data
Security and compliance
Uptime
Development time
Early warning on possibile issues and failure
Notification of failure
Trend analysis
Measure of metrics

Copyright © 2017 Accenture All rights reserved.
How to start
2
We have to decide what to measure. Measure all is equivalent to measure nothing.
• Latency time, response time, QA, availability
Train the ML
• Focus to eliminate false positive
Give significance to correlation
• Gain visibility across silos
Since we don’t know why the ML respond this way…
start again!

Copyright © 2017 Accenture All rights reserved.
How test the systen
2
Define acceptance criteria
Test again with new data
Results are not perfect, but close enough
Be aware of the level of confidence when you use the prediction

Copyright © 2017 Accenture All rights reserved.
Conclusion
2
• Devops produces huge amounts of data: the natural domain for ML
• More data implies more decision to take
• In a rapid changing scenario traditional monitoring based on fixed algorythms is
not so paying back
• ML discover trends

Copyright © 2017 Accenture All rights reserved.
Thank you!
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