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.
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.
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.
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.
Walk through new features for making it easier to create useful machine learning jobs in 6.1 with the new Data Visualizer and modules.
The newest X-Pack feature in 6.1 is on-demand forecasting. Machine learning can model the data and predict multiple time intervals into the future.
The sizing of hardware and cluster for machine learning depends on the use-case. We can guide you through best practices to use for your ML needs.
An introduction to how IT Operations can take advantage of Elastic's machine learning.
Spin up a fully loaded deployment on the cloud provider you choose. As the company behind Elasticsearch, we bring our features and support to your Elastic clusters in the cloud.