Recommended
Recommended
More Related Content
Similar to Introduction to machine learning using Elastic
Similar to Introduction to machine learning using Elastic (20)
More from FaithWestdorp
More from FaithWestdorp (16)
Recently uploaded
Recently uploaded (20)
Introduction to machine learning using Elastic
- 1. Introduction to machine learning in Elastic Search. Observe. Protect. Tom Grabowski, ML Product Manager Camilla Montonen, Elastic ML data scientist Jenny Morris, Solutions Architect June 25, 2020
- 3. Housekeeping & Logistics ● Attendees are automatically muted when joining Zoom webinar ● Q+A will be at the end of the webinar ● Ask questions for us in the Zoom chat during the webinar ○ Chat settings To: All panelists and attendees ○ Ask more questions on our discuss forum: discuss.elastic.co ● Recording will be available after the webinar and on the event page
- 4. Elastic is a search company. Scale RelevanceSpeed
- 5. Three Solutions. Elastic Enterprise Search Elastic SecurityElastic Observability
- 6. One Stack. Elastic Enterprise Search Elastic SecurityElastic Observability Kibana Elasticsearch Beats Logstash
- 7. Deploy Anywhere. Elastic Enterprise Search Elastic SecurityElastic Observability Kibana Elasticsearch Beats Logstash Elastic Cloud Elastic Cloud on Kubernetes Elastic Cloud Enterprise Self-Managed Elastic Stack
- 8. Elastic Machine Learning Operationalize and Simplify data science
- 9. Time Series Anomaly Detection Data Frame Analysis
- 10. Machine Learning Anomaly Detection 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 10 years development & Industry leading technology
- 11. 111 Predict Expected value @ 15:05 = 1859 Learn Operationalize
- 12. Solution integrations Anomaly detection within the Solutions workflow APM Logs SIEM Uptime
- 14. Machine Learning expanding use cases Unsupervised and supervised machine learning Unsupervised Supervised Data Driven: Pattern Recognition Labelled data for Learning and Predicting Anomaly Detection Outlier Detection Forecasting Language ID Fraud Detection User classification
- 15. 15 What behaviour can you learn from to make predictions? • ML in search – What language is this document written in? – How can I boost search relevance for named entities? – What search results are most relevant based on click-through rate? • Observability – What users or hosts are outliers? – How can I Classify alerts and route them to the right team? – What customers are likely to churn? • Security – How can I identify malicious domain names generated by DGAs? – How can I classify activity as originating from a device type (e.g. router version)? Why build your own ML models?
- 16. Using Supervised Learning for DGA Identification
- 17. Elastic is a Search Company. www.elastic.co Thank You
- 18. SLED Virtual User Group Learn more about Elastic and represent your community at the next Elastic Virtual Group - July 23 @ 2 PM ET Monitoring and preventing threats as employees transition from home to office As employees return to offices or continue to work from home, the attack surface increases exponentially. How will you protect your network and infrastructures with new users, changes to roles and permissions, and high volumes of attack attempts? Get insights from Salt Lake County for protecting their systems with insights on more comprehensive monitoring and logging, improved alerting, and more! View Details
- 19. Questions?
- 20. Elastic is a search company.
- 21. Tom Grabowski ML Product Manager Elastic Camilla Montonen ML Data Scientist Elastic Jenny Morris Solutions Architect Elastic