- Combine machine learning with the analytic capabilities of Elastic Stack
- Analyze large volumes of search data and gain actionable insight from them
- Use external analytical tools with your Elastic Stack to improve its performance
Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.
As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.
By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
- Install the Elastic Stack to use machine learning features
- Understand how Elastic machine learning is used to detect a variety of anomaly types
- Apply effective anomaly detection to IT operations and security analytics
- Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting
- Combine your created jobs to correlate anomalies of different layers of infrastructure
- Learn various tips and tricks to get the most out of Elastic machine learning
If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
- Combine machine learning with the analytic capabilities of Elastic Stack
- Analyze large volumes of search data and gain actionable insight from them
- Use external analytical tools with your Elastic Stack to improve its performance
Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.
As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.
By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
- Install the Elastic Stack to use machine learning features
- Understand how Elastic machine learning is used to detect a variety of anomaly types
- Apply effective anomaly detection to IT operations and security analytics
- Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting
- Combine your created jobs to correlate anomalies of different layers of infrastructure
- Learn various tips and tricks to get the most out of Elastic machine learning
If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
Machine Learning with the Elastic Stack
304
Machine Learning with the Elastic Stack
304
Product Details
| ISBN-13: | 9781788477543 |
|---|---|
| Publisher: | Packt Publishing |
| Publication date: | 01/30/2019 |
| Pages: | 304 |
| Product dimensions: | 7.50(w) x 9.25(h) x 0.64(d) |