Articles by Tom Grabowski

Principal Product Manager, Elastic


Elastic Observability 8.10: Elastic AI Assistant enhancements and GA of Universal Profiling

Elastic Observability 8.10 introduces the general availability release of Elastic Universal Profiling™ and enhancements to the Elastic AI Assistant for Observability.


Elastic Observability 8.9: Introducing AI Assistant and up to 70% cost savings on metrics storage!

Elastic Observability 8.9 introduces new AI Assistants for APM, logs, hosts, alerts, and profiling. In addition, new Time Series Data Stream (TSDS)-ready Elastic Agent integrations unlock storage efficiencies optimizing storage cost by up to 70%.


Elastic Observability 8.8: Efficient operations with GitOps-based synthetics monitoring and direct AWS Firehose ingest

Elastic Observability 8.8 delivers the general availability release of synthetic monitoring, the ability to ingest data directly to Elastic Cloud from Amazon Kinesis Firehose, and new capabilities for managing service level objectives.


Using AIOps for automation and efficiency in observability and IT operations

Common use cases and scenarios for a successful AIOps deployment within observability. From monitoring to anomaly detection and root cause analysis, understand how you can improve your AIOps deployment and see success.


Introduction to modern natural language processing with PyTorch in Elasticsearch

In 8.0, you can now upload PyTorch machine learning models into Elasticsearch to provide modern natural language processing (NLP). Integrate one of the most popular formats for building NLP models and incorporate them as part of a NLP data pipeline.


Detecting rare and unusual processes with Elastic machine learning

To secure your environment, Elastic Security has many out-of-the-box machine learning configurations for detecting rare activity, networks, and processes, as well as tools to customize your own anomaly detection jobs.


Using Elastic machine learning rare analysis to hunt for the unusual

Learn how Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data.


Designing for Change in Elastic Machine Learning


Introducing data visualization and modules in machine learning

Walk through new features for making it easier to create useful machine learning jobs in 6.1 with the new Data Visualizer and modules.