In May we released the Splunk Machine Learning Toolkit (MLTK) version 5.2. We’ve loved telling you about some of the great new features, including the most recent blog on DensityFunction. However, we know that before you can start experimenting with model-building algorithms such as DensityFunction, your data needs to be prepared for machine learning. Machine learning operates best when you provide clean data as the foundation for building your models.
As a ServiceNow manager, have you ever wondered how you can deliver a satisfying customer experience to your end users without increasing headcount, or adding to the workload of your existing front-line workers? In a world of digital transformations and disruptions, providing swift and complete customer service is about finding and fixing gaps in existing incident management processes using data analytics.
Modern cloud storage tools such as Dropbox give teams the ability to easily share and centralize content, conveniently collaborate on projects, and sync data across devices. They’ve proven to be real productivity enhancers, especially with the expansion of work-from-home workforces. But cloud storage tools often end up being a dumping ground for lots of content and various clutter, making it clumsy at best (and next to impossible at worst) to find anything.
The 7.6 release of the Elastic Stack delivered the last piece required for an end-to-end machine learning pipeline. Previously, machine learning focused on unsupervised techniques with anomaly detection. However, several features have been released over the 7.x releases. In 7.2 Elasticsearch released transforms for turning raw indices into a feature index. Then 7.3, 7.4, and 7.5 released outlier detection, regression, and classification, respectively.
SAN FRANCISCO — July 22, 2020 — InfluxData, creator of the time series database InfluxDB, today announced that InfluxDB Cloud is now available on Microsoft Azure, furthering the company’s commitment to increase accessibility to developers. With this announcement, InfluxDB Cloud is now live on all three major cloud platforms — Microsoft Azure, Google Cloud and Amazon Web Services.
Welcome to our series on cost management and optimization in Elasticsearch Service. With the increased functionality in Elastic Cloud, it is now easier than ever to utilise many of the free and open features of the Elastic Stack to optimise your cloud deployment. This blog is a great resource for reviewing your existing high availability and data management strategies when it comes to cost management.
Our new Elastic for Students and Educator program provides online resources and support to help you teach and learn no matter where you are. Hear from Luis Francisco Sánchez Merchante, an educator based in Spain, as he reflects on the challenges he’s faced while teaching during a global pandemic.
Aside from ensuring each service is working properly, one of the most challenging parts of managing a cloud-based infrastructure is cost monitoring. There are countless services to keep track of—including storage, databases, and computation—each with their own complex pricing structure. Monitoring cloud costs is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time and accurately forecast monthly costs.
Searches are integral parts of any application. Performing searches on terabytes and petabytes of data can be challenging when speed, performance, and high availability are core requirements. This blog post will pit Solr vs Elasticsearch, two of the most popular open source search engines whose fortunes over the years have gone in different directions. Both of them are built on top of Apache Lucene, so the features they support are very similar.
