The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Tickets

Age of AI Ops

Submitted by Nitin Gupta (@nitin163) on Wednesday, 29 May 2019

Session type: Full talk of 40 mins Session type: Full talk of 40 mins

View proposal in schedule

Abstract

We look at the evolution and rise of AI Ops. AIOps is the technology solution leveraging machine learning and data analytics to help automate how we react to issues in real time across layers of infrastructure and software.

Outline

In this session, we go over * Key attributes of AIOps * Technology layers * Problems that AIOps promises to address and case studies * Reference architecture * Road ahead

Speaker bio

Nitin Gupta works at Appdynamics, leading the AppDynamics Data Platform from Bangalore. Earlier, he spent 10 years at Microsoft, had a short stint with his own startup and then led Data and User platforms at Inmobi technologies. He has been working with large scale distributed data processing systems over the last 8 years.

Slides

https://www.slideshare.net/secret/9r93pC2Y1UYkMB

Comments

  • Abhishek Balaji (@booleanbalaji) Reviewer 4 months ago

    Hi Srikanth,

    Thank you for submitting a proposal. We need to see detailed slides and a preview video to evaluate your proposal. Your slides must cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/frameworks available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?

    We need your updated slides and preview video by Jun 10, 2019 to evaluate your proposal. If we do not receive an update, we’d be moving your proposal for evaluation under a future event.

  • Nitin Gupta (@nitin163) Proposer 3 months ago
    • Problem Statement: AIOps is an umbrella term used for Algorithmic IT Operations and an emerging tech for applying machine learning and artificial intelligence to whole class of problems in monitoring and trouble shooting of large scale production environments. Since the monitoring data tends to be large, this is classic example of “applied big data analytics problem”. This is not invented specifically by AppDynamics, but a collection of ideas and techniques can be applied by anyone who has large infrastructure and software portfolio to manage. See https://en.wikipedia.org/wiki/IT_operations_analytics
    • Goals of the talk: a) Many are unclear or new to this as area of innovation where big data analytics can be applied for problems in managing their own infrastructure / software. First goal is to introduce the problem and highlight how and why this is an analytics problem involving big data and data sciences
      b) Share details of how if one where to use this in their production deployments and improve their service availability and reduce MTTR taking one example such as metric baselining or root cause analysis (for ex)
    • Intended Audience: Any engineer running and managing large enough infrastructure would find the solutions appealing & interesting. And more importantly, techniques such as baselining, anomaly detections or root cause analysis used with AIOps may resonate well with problems which are similar, in other domains
    • Applicability/Key Takeaway: Anyone who has a medium to complex infrastructure / software complexity can gain from ML applied to the problem (if done correctly). Some ideas discussed during the talk can be a starting point, especially if they are completely new to this
    • Before/After scenarios: If applied correctly, the promise is immense and this is an emerging field. Exposure to this with large data science audience helps in wider population adopting this and similarly existing practitioners in the field can gain from the experiences of others from different domain, but have solved problems of similar nature.
    • Zainab Bawa (@zainabbawa) Reviewer 3 months ago

      Thanks for the clarifications, Nitin. This is helpful.

      On the structure of the talk and the scope of the content, will you be citing examples and case from AppDynamics (or other organizations) to explain the idea? Concrete examples are always very helpful for setting the context, identification of the concept among different audience sets (the aha! moment), and for driving home the point of why to consider AI Ops?

  • Nitin Gupta (@nitin163) Proposer 3 months ago

    Yes, I will be citing specific examples to introduce the problem statement and will also cover focus areas for AppDynamics under the AI Ops umbrella

    • Zainab Bawa (@zainabbawa) Reviewer 3 months ago

      Thanks Nitin. I have sent a pre-event rehearsal invite now. Look forward to a walk through session of your presentation.

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