Bringing AI/ML into Technology Operations-GlueCon-2019-kiranchitturi-public

© 2019 Sungard Availability Services, all rights reserved
Bringing AI/ML into Technology
Operations
Kiran Chitturi
CTO Architect, Sungard Availability Services
@nkchitturi
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Sungard AS Portfolio
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4© 2019 Sungard Availability Services, all rights reserved Public
Agenda
• Intro
• Categories that can benefit by leveraging AI/ML with point solution ideas
• Gotchas / Challenges for maturity
• End-End Pipeline example
• Final Thoughts

Intro
• Operations - looking for better ways to manage your infrastructure
• detect the root causes of issues faster
• predict failures and impact
• prevent issues
• "Predictions are hard, especially about the future.”
– Yogi Berra or Neils Bohr, depending whether you prefer Baseball or Physics

6© 2019 Sungard Availability Services, all rights reserved
What is Machine Learning (ML)
• Get answers from your data
• Examples
• Stages
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PREPARING
THAT DATA
TUNING
TRAININGGATHERING
DATA
CHOOSING A
MODEL
PREDICTIONEVALUATION

Vendor Packaging Types Learning Types
• Supervised
• Unsupervised
• Semi-supervised
• Reinforcement
• Algorithm aspects
• Linear
• Neural Networks/Deep Learning
• Ensemble
• etc.
Types of Machine Learning
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BLACK BOX ML
OPEN BOX ML

Image from xkcd

In Operations, using ML might look like…
• Collecting – Various types of Logs
• Learning - Patterns, Tools and Data
• Insights
• Predicting and Preventing
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TRAININGINGEST INSIGHTS PREDICT /
PREVENT
CONTINUOUS LEARNING

Categories in Operations
• Service Desk
• Logging & Monitoring
• Automation - Infrastructure / Configuration Management
• Cost Optimization / Capacity management
• Security / Compliance
• Business Intelligence / Analytics

ML in Service Desk
• Categorization of requests
• Assignment of requests (classification & routing)
• Anomaly detection and forecasting
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• Other possible Point Solutions integrated with your Service
Desk
• helping parse & route support related emails
• chatbots - employee self service, customer contacts,
context based recommendations

ML in Logging & Monitoring
• Smarter alerts
• categorized, abnormal dips in user traffic, periodic fluctuations etc.
• For better correlation/root cause abilities, include APM (Application Performance
Monitoring)
• Vendor Aspects
o Operational Analytics Tools
o Overarching AIOPS platforms
o ML Toolkits make it easier - command executions, visualizations, pre-defined
end-end solutions and customizations
• Integrated with Security monitoring and DevOps workflows
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ML in Automation
• Proactive remediation
• Benefits come from enabling AI/ML in Service Desk and Monitoring and
integrating into Automation
• For orchestrating your workflow automations, you can leverage your
Service Desk as a hub
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ML in Cost Optimization
• Detecting unusual Billing Activity, Predicting Cost
• Right-sizing workloads
• Reliably utilize Spot Instances to maintain high-availability - based
on ML models to predict Spot Instances interruption
• Self-manage workloads; hybrid environments;
• Build continuous cloud spend optimization into Agile/DevOps
processes
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ML in Security & Compliance
• Analyze data and network interactions for safe (IOT) device behavior
• Data sharing monitoring
• Threat Detection
• Predicting Threats or Malware
• Account for
• as you begin your ML adventures, you might have too many
notifications and/or false positives – let the model learn your
environment and mature
• protecting the integrity of your knowledge base
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Business Intelligence / Analytics
• Provide context-aware suggestions
• Auto-generating and prioritizing analytics and insights based on
the overall data set and a user’s search criteria
• Generating more relevant suggestions based on factors such as
• user behavior and feedback
• community trends
• industry knowledge etc.
• Processing complex/unstructured input
• Dynamic Data
• Richness – Correlated mixed type streams
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Operationalizing Machine
Learning
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Typical ML Workflow Challenges
• Various tools for different phases of lifecycle
• Dynamic Data
• Retraining
• Reproducing steps and results
• Tracking your
• Data
• Models
• Algorithm aspects
• Non-Intuitive Complexity
• Heterogenous
• Mix of Skills
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An example of an end-to-end ML Pipeline
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Using Kubeflow,
Pachyderm and Seldon

Thoughts and Guardrails for Operationalizing
• The business side of your ML app
• Success metrics
• Data silos
• Partnering with org functions
• Visibility & Access Needs needs – Data Scientists, Operators, Business Analysts
• “Productionalizing”
• Monitoring - Detect Data deviation, drift, Canary pipelines, A/B testing
• Governance - Policy, Human approval
• Automation – Retraining Pipelines, Propagation, Multiple Pipelines
• Scale – Training – GPUs/ CPU clusters, Inference Implementation
• Develop Skillsets
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Summary
• Machine Learning in your Technology Operations can mean
• faster searches, getting to root causes quicker and taking
intelligent actions
• freeing you up to do more interesting things
• Operationalizing ML needs adequate diligence
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Additional Resources - Blog blog.sungardas.com/CTOLabs/

Q A
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Appendix
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References
• Kubeflow Pachyderm & Seldon -
https://github.com/kubeflow/examples/tree/master/github_issue_summarization/Pachyd
erm_Example
• Images
o Model Training comic Image inspired from xkcd
o help desk by jai from the Noun Project
o activity by Gregor Cresnar from the Noun Project
o automation by Vector Studio from the Noun Project
o cost Optimization by Seanau from the Noun Project
o Security by icons from the Noun Project
o Data Analysis by Srinivas Agra from the Noun Project
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Bringing AI/ML into Technology Operations-GlueCon-2019-kiranchitturi-public

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