Realizing your AIOps goals with machine learning in Elastic
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The document discusses the implementation of AIOps using Elastic Machine Learning, highlighting the platform's features such as anomaly detection, data ingestion, and real-time analysis. It addresses the challenges faced by large organizations like Cerner in infrastructure monitoring and emphasizes the use of supervised machine learning techniques to predict customer behavior and reduce operational costs. The presentation also includes forward-looking statements about the company’s future offerings and market strategies.
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Transform raw data to a feature index
Machine Learning end-to-end methodology
Define a ML
problem and
propose a
solution
Construct your
dataset
Transform
data
Train a model
Use the model
to make
predictions
{
"customer_id": "028fa21e",
"session_id": "MA0l6PC5",
"@timestamp": "2019-05-08T18:46:22",
"request_type": "streaming_tv",
"channel": "bbc",
"title": "Line of Duty"
},
{
"customer_id": "a4ca7c7c",
"session_id": "LMSXQXHg",
"@timestamp": "2019-05-08T18:49:34",
"request_type": "streaming_film",
"channel": "ziggo",
"title": "Glass"
},
...
PUT _transform/customer_behaviour
{
"source": {
"index": ["viewing_logs"]
},
"description": "Pivot viewing logs to customer-centric index",
"dest": {"index": "customer_behaviour"},
"pivot": {
"group_by": {
"customer_id": {"terms":{"field": "customer_id"}
}
},
"aggregations": {
"total_tv_shows": {...},
"total_films": {...},
...
}
}
}
{
"customer_id": "028fa21e",
"total_tv_shows": 10,
"total_films": 2,
"total_watching_duration": 72123,
"last_active": "019-05-08T18:46:22",
...
},
{
"customer_id": "a4ca7c7c",
"total_tv_shows": 23,
"total_films": 8,
"total_watching_duration": 184212,
"last_active": "2019-05-08T18:49:34",
...
},
...
RAW Data Customer Index](https://image.slidesharecdn.com/realizingyouraiopsgoalswithelasticml-201030044203/85/Realizing-your-AIOps-goals-with-machine-learning-in-Elastic-15-320.jpg)
Realizing your AIOps goals with machine learning in Elastic
- 1. Realizing your AIOps goals with Elastic Machine Learning Mukesh Gadiya | Sr. Manager, Product Management Tom Grabowski | Principal Product Manager Jim Avazpour | Director of Infrastructure @Cerner
- 2. This presentation and the accompanying oral presentation contain forward-looking statements, including statements concerning plans for future offerings; the expected strength, performance or benefits of our offerings; and our future operations and expected performance. These forward-looking statements are subject to the safe harbor provisions under the Private Securities Litigation Reform Act of 1995. Our expectations and beliefs in light of currently available information regarding these matters may not materialize. Actual outcomes and results may differ materially from those contemplated by these forward-looking statements due to uncertainties, risks, and changes in circumstances, including, but not limited to those related to: the impact of the COVID-19 pandemic on our business and our customers and partners; our ability to continue to deliver and improve our offerings and successfully develop new offerings, including security-related product offerings and SaaS offerings; customer acceptance and purchase of our existing offerings and new offerings, including the expansion and adoption of our SaaS offerings; our ability to realize value from investments in the business, including R&D investments; our ability to maintain and expand our user and customer base; our international expansion strategy; our ability to successfully execute our go-to-market strategy and expand in our existing markets and into new markets, and our ability to forecast customer retention and expansion; and general market, political, economic and business conditions. Additional risks and uncertainties that could cause actual outcomes and results to differ materially are included in our filings with the Securities and Exchange Commission (the “SEC”), including our Annual Report on Form 10-K for the most recent fiscal year, our quarterly report on Form 10-Q for the most recent fiscal quarter, and any subsequent reports filed with the SEC. SEC filings are available on the Investor Relations section of Elastic’s website at ir.elastic.co and the SEC’s website at www.sec.gov. Any features or functions of services or products referenced in this presentation, or in any presentations, press releases or public statements, which are not currently available or not currently available as a general availability release, may not be delivered on time or at all. The development, release, and timing of any features or functionality described for our products remains at our sole discretion. Customers who purchase our products and services should make the purchase decisions based upon services and product features and functions that are currently available. All statements are made only as of the date of the presentation, and Elastic assumes no obligation to, and does not currently intend to, update any forward-looking statements or statements relating to features or functions of services or products, except as required by law. Forward-Looking Statements
- 3. By 2023, 40% of DevOps teams will augment app and infra monitoring tools with AIOps platform capabilities to decrease mean time to problem resolution and the resultant operational costs. Gartner Market Guide for AIOps Platform, November 2019
- 4. 4 What constitutes an AIOps platform?* • Ingesting data from various sources for cross-domain analysis • Real time anomaly detection based on historical data analysis • Storing and providing access to the raw data • Suggesting prescriptive responses to analysis • Initiating an action or next step based on the prescription * Gartner Market Guide for AIOps Platform, November 2019
- 5. 5 * Gartner Market Guide for AIOps Platform, November 2019 What constitutes an AIOps platform?* • Ingesting data from various sources for cross-domain analysis • Real time anomaly detection based on historical data analysis • Storing and providing access to the raw data • Suggesting prescriptive responses to analysis • Initiating an action or next step based on the prescription
- 6. 6 Machine Learning Anomaly Detection 10 years development & industry leading technology Unsupervised machine learning Automatically detect anomalies, outliers from group, and rare events Sophisticated ML Job UI Interactive views of model and anomaly scoring Root cause analysis Report on factors influencing anomalies On-Demand Forecasting Forecast out time series metrics
- 7. AIOps outcomes enabled by Elastic Machine Learning • Reduce MTTR for SREs (Demo) – Alert noise reduction – Anomalies correlation – Root cause analysis – Log categories • Reduce time to value for Dev teams – Auto-grok for custom log parsing – One click ML integration in APM, Logs, Infra metrics and Synthetics
- 8. Observability + Machine Learning Cerner
- 9. • Cerner Corporation is a supplier of healthcare information technology HCIT systems, services, devices and hardware – 29,000 employees in 30 countries • Cerner’s Millennium Service provides Electronic Medical Record (EMR access to 27,000 customers (hospitals, doctors, etc.) in 26 countries • Cerner Network – 19 Data Centers (11 in US – Carrier Grade Network – 170,000 servers – 1,900 circuits – Nationwide fiber rings – 560,000 network ports – 500 Remote Hosted Clients – 260,000 Peak Concurrent Users Who is Cerner?
- 10. Large Scale Infrastructure Monitoring Challenges • Data Volume – Require cost-effective, scalable and resilient ingestion platforms • Gaps In Monitoring Resolutions – Data Feed 1 DF1 industry standard alert intervals are set too high to reduce noise – Data Feed 2 DF2 every violation must generate an alert – DF1 vs DF2 not all alerts need to be console bound • Lower MTTK & MTTR – Alert tagging for service to resource mapping – Grouping and categorizing service-related violations • Utilizing Machine Learning – Baselining, Deviation from normal – Identifying abnormalities
- 11. Thank You! • Sign up on Elastic Cloud and try the power of Elastic ML ○ https://cloud.elastic.co/registration • Elastic ML Case studies ○ Cerner, TMobile, Sky, PostBank, ETrade, IHG • Elastic ML Forum ○ https://discuss.elastic.co/tag/stack-machine-learning
- 12. Appendix Additional slides for supervised learning walkthrough
- 13. Delivering ML solutions throughout the data science process Machine Learning end-to-end methodology Define a ML problem and propose a solution Construct your dataset Transform data Train a model Use the model to make predictions Elastic Stack delivers an end-to-end machine learning pipeline providing the path from raw data to building, testing, and deploying machine learning models in production
- 14. 14 Which customers are likely to churn? Machine Learning end-to-end methodology { "customer_id": "028fa21e", "session_id": "MA0l6PC5", "@timestamp": "2019-05-08T18:46:22", "request_type": "streaming_tv", "channel": "bbc", "title": "Line of Duty" } { "customer_id": "a4ca7c7c", "session_id": "LMSXQXHg", "@timestamp": "2019-05-08T18:49:34", "request_type": "streaming_film", "channel": "ziggo", "title": "Glass" } { "customer_id": "avad97s3", "session_id": "LMSXQXHg", "@timestamp": "2019-05-08T18:50:34", "request_type": "streaming_film", "channel": "ziggo", "title": "Glass" } { "customer_id": "dce909a0", "session_id": "MA0l6PC5", "@timestamp": "2019-05-08T18:51:23", "request_type": "streaming_film", "channel": "ziggo", "title": "Glass" } { "customer_id": "vfva09a09", "session_id": "LMSXQXHg", "@timestamp": "2019-05-08T18:52:14", "request_type": "streaming_film", "channel": "ziggo", "title": "Glass" } { "customer_id": "sdfd9s90", "session_id": "MA0l6PC5", "@timestamp": "2019-05-08T18:54:51", "request_type": "streaming_film", "channel": "ziggo", "title": "Glass" } ... Data is often raw logs Define a ML problem and propose a solution Construct your dataset Transform data Train a model Use the model to make predictions Customer behavior is often described by aggregate features
- 15. 15 Transform raw data to a feature index Machine Learning end-to-end methodology Define a ML problem and propose a solution Construct your dataset Transform data Train a model Use the model to make predictions { "customer_id": "028fa21e", "session_id": "MA0l6PC5", "@timestamp": "2019-05-08T18:46:22", "request_type": "streaming_tv", "channel": "bbc", "title": "Line of Duty" }, { "customer_id": "a4ca7c7c", "session_id": "LMSXQXHg", "@timestamp": "2019-05-08T18:49:34", "request_type": "streaming_film", "channel": "ziggo", "title": "Glass" }, ... PUT _transform/customer_behaviour { "source": { "index": ["viewing_logs"] }, "description": "Pivot viewing logs to customer-centric index", "dest": {"index": "customer_behaviour"}, "pivot": { "group_by": { "customer_id": {"terms":{"field": "customer_id"} } }, "aggregations": { "total_tv_shows": {...}, "total_films": {...}, ... } } } { "customer_id": "028fa21e", "total_tv_shows": 10, "total_films": 2, "total_watching_duration": 72123, "last_active": "019-05-08T18:46:22", ... }, { "customer_id": "a4ca7c7c", "total_tv_shows": 23, "total_films": 8, "total_watching_duration": 184212, "last_active": "2019-05-08T18:49:34", ... }, ... RAW Data Customer Index
- 16. Build a model on historical data that has a churn indicator Machine Learning end-to-end methodology Define a ML problem and propose a solution Construct your dataset Transform data Train a model Use the model to make predictions customer a customer b total duration of customer sessions 80:21:07 1:01:11 tv episodes watched 24 1 films watched in last month 5 0 newness of titles watched in last month 9.8 1.2 Change in duration 6:22:17 16:43:29 subscription plan gold platinum customer tenure 32 26 has churned? no yes ML Supervised Model train/validate/test Model Name: churn_e2r21 Model Precision: 96.3% Model Recall: 95.7% Model F1 score: 96.0%
- 17. 17 Use model inference to make predictions on streaming data Machine Learning end-to-end methodology Define a ML problem and propose a solution Construct your dataset Transform data Train a model Use the model to make predictions customer c total duration of customer sessions 10:10:06 tv episodes watched 2 films watched in last month 1 newness of titles watched in last month 1.6 change in duration this month 17:22:17 customer plan gold customer tenure 5 customer c Feature Influence total duration of customer sessions 10:10:06 0.1 tv episodes watched 2 0.8 films watched in last month 1 0.8 newness of titles watched in last month 1.6 0.01 change in duration of this month 17:22:17 0.6 customer plan gold 0.01 customer tenure 5 0.1 will churn? p(churn) = 97% ML Supervised Model predict
- 18. 18 Elastic + Jupyter Notebooks pip install eland https://github.com/elastic/eland http://eland.readthedocs.io/
- 19. Demo
- 20. Thank You!