Time Series Anomaly Detection: Optimizing your Machine Learning Jobs in Elasticsearch

Got logs and metrics (or any time stamped data really) in Elasticsearch, and looking to craft your 1st machine learning job to automate anomaly detection? Watch a practical walkthrough on efficiently progressing your machine learning projects from concept to production.

Elastic Machine Learning experts kick off the presentation with the Elasticsearch and machine learning basics, and then demomstrate the iterative process of crafting and optimizing your machine learning jobs.

Highlights include:

  • Leveraging Kibana visualization tools to build intuition about the data
  • Using scheduled events to exclude expected anomalies from data / model
  • Forecasting into the future using the trained model
  • Getting proactive via alerting integrations

Additional Resources (Related content)



Rich Collier

Joining the Elastic team from the Prelert acquisition, Rich has over 20 years experience as a Solutions Architect / Pre-Sales Systems Engineer for software, hardware, and service-based solutions. Rich’s technical specialties include: Big data analytics, Machine learning, Anomaly detection, Threat detection, Security Operations, Application Performance Management, Web Applications, and Contact Center Technologies. Rich is based in Boston, Massachusetts.

Thomas Grabowski

Thomas Grabowski is a member of the Product Management team and focuses on X-pack, Machine Learning. Prior to joining Elastic, he has spent the last two decades in IT Log Operations and Analytics market. Thomas co-founded two companies, LogLogic and RapidEngines, which were both acquired.