2. Abstract/Session Description:
Session Title:
Machine Learning Turbo-Charges the Ops Portion of DevOps
Abstract/Session Description:
Moving toward continuous (or short-cycle) delivery? Constantly rewiring your apps with microservice and similar architectures? How are you planning to maintain visibility and maximize service levels once this stuff gets into production? Coding instrumentation into your apps is time-consuming and error prone. Instead, let machine learning do the work of adapting your monitoring to your fast-moving application environments. In this session, we’ll discuss different types of machine learning that are optimized for operational data, and how they are leveraged to ensure your Ops moves as fast as the rest of your DevOps pipeline.

3. Machine Learning Turbo-Charges the Ops Portion of DevOps
Oracle Code
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Dan Koloski
Senior Director
Oracle Management Cloud
March, 2017

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5. Program Agenda Defining terms1
Defining terms
Why (Dev)Ops is Perfect for Machine Learning
Making Machine Learning Smarter
Q&A 2 3 4

6. Program Agenda Defining terms1
Defining terms
Why (Dev)Ops is perfect for machine learning
Making Machine Learning Smarter
Q&A 2 3 4

7. Defining Terms (source: wikipedia.com)
Machine Learning
Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data.
DevOps
DevOps (a clipped compound of "software DEVelopment" and "information technology OPerationS") is a term used to refer to a set of practices that emphasize the collaboration and communication of both software developers and information technology (IT) professionals while automating the process of software delivery and infrastructure changes.
Systems Management or IT Operations Management
IT Operations is responsible for the smooth functioning of the infrastructure and operational environments that support application deployment to internal and external customers, including the network infrastructure; server and device management; computer operations; IT infrastructure library (ITIL) management; and help desk services for an organization.

8. Program Agenda Defining terms1
Defining terms
Why (Dev)Ops is perfect for machine learning
Making Machine Learning Smarter
Q&A 2 3 4

9. We have a problem: Dev has outpaced Ops
Development is creating faster…
Low-code
Agile
Microservices CI
(Dev)Ops is promoting faster…
Containers
IaaS & PaaS CD
Packages
(the rest of)Ops is not moving any faster… …

10. One of Two Likely Outcomes, Both Bad
OPTION 1:
Your Changes Don’t Hit Production Until Ops is Ready
Option 2:
You Promote Unmanaged Code Anyway
Confidential – Oracle Internal/Restricted/Highly Restricted

11. The Reason: Ops Depends on Human Effort
Part of our problem is that the industry state of the art for many years has relied too heavily on human effort.
We deploy 10s or 100s of monitoring silos and then expect humans to not only find the data, but understand it and know what it means, in time-compressed scenarios.
The net result is that we are always behind, and “running to catch up.”
Where’s the data?
What does the data mean?

12. We Can Help! Ops Data is Perfect for Machine Learning
Massive volume
Highly patterned
Predictable format
Silos can be unified
Seasonal trends
Known sources
Structured, Time-Series
User Performance Metrics
Server-side Performance Metrics (App & Infrastructure)
Configurations
Events/Alerts
Transaction Payloads
Unstructured Text
Log Records
It’s easy to see why IT organizations can’t keep up. They are drowning in monitoring tools and data, but have no insight.
Previous generations of management tools expect that human intelligence will draw conclusions out of the data, but human operators are already overwhelmed with the velocity and volume of alerts and data coming their way, and so they get tired and miss things.
Other tools have attempted to apply more advanced analytical techniques, but have only done so to subsets of the data because it’s too compute-intensive to do it across the board, so human operators are still required to stitch together information out the data silos.
It’s time for a new generation of systems management.

13. Algorithmic Approaches to IT Ops Data
Structured, Time-Series
User Performance Metrics
Server-side Performance Metrics (App & Infrastructure)
Configurations
Events/Alerts
Transaction Payloads
Unstructured Text
Log Records
Anomaly detection
clustering
It’s easy to see why IT organizations can’t keep up. They are drowning in monitoring tools and data, but have no insight.
Previous generations of management tools expect that human intelligence will draw conclusions out of the data, but human operators are already overwhelmed with the velocity and volume of alerts and data coming their way, and so they get tired and miss things.
Other tools have attempted to apply more advanced analytical techniques, but have only done so to subsets of the data because it’s too compute-intensive to do it across the board, so human operators are still required to stitch together information out the data silos.
It’s time for a new generation of systems management.
correlation
prediction 13

14. Program Agenda Defining terms1
Defining terms
Why (Dev)Ops is perfect for machine learning
Making Machine Learning Smart for IT Ops
Q&A 2 3 4

15. Maturing Machine Learning: A Three-Step Approach
ML is not smart out of the box for every question
To make ML smarter, know the questions you want to ask, then…
Enhance Algorithms
Increase Breadth
Increase Depth

16. ML Must Be Tuned To The Questions We Want To Ask
What caused the problem?
How do I prevent the problem in the future?
Is what I’m seeing normal or abnormal?
What areas can I improve, and how?
Most importantly, we know what questions we want to ask, so we can pre-tune the machine learning to find the answers.
This is a very purpose-built set of machine learning, not just general data science.
How is this application actually built/architected?
What do I need to pay attention to right now?
How should I rebalance workloads?
WHAT WILL HAPPEN TOMORROW?

17. DEMO: Matured Machine Learning in Action

18. Increase Breadth With Data Unification & Normalization
Application
Performance
Monitoring
Security Monitoring & Analytics
Infrastructure Monitoring
Log Analytics
Orchestration
Compliance
Oracle Management Cloud Data Store
Norm is repo by repo projects: slow and incremental.
By centralizing data, we are able to deliver ML driven features more quickly.
Convert to Time Series
(Clustering & Rollup)
Base Lining &
Anomaly Detection
IT Analytics

19. Increase Depth With Context, Topology, & Domain Expertise
Topology: Tells us where to look.
Context: Forecasted SLA violation & observe divergent correlation.
Domain Expertise: Allows to identify root cause.

20. DEMO: Enhanced Breadth with Unified Data, Enhanced Depth with Context

21. Key Takeaways DevOps depends on “Ops” speed matching “Dev” speed
The DevOps problem is well-suited to machine learning BUT…
Machine Learning must be matured
Unified data and context increases the effectiveness of ML and analysis
Confidential – Oracle Internal/Restricted/Highly Restricted

22. Program Agenda 1
Defining terms Why (Dev)Ops is perfect for machine learning Making Machine Learning Smarter Q&A 2 3 4

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