Machine Learning


Detecting unusual network activity with Elastic Security and machine learning

As we’ve shown in a previous blog, search-based detection rules and Elastic’s machine learning-based anomaly detection can be a powerful way to identify rare and unusual activity in cloud API logs. Now, as of Elastic Security 7.13, we’ve introduced a new set of unsupervised machine learning jobs for network data, and accompanying alert rules, several of which look for geographic anomalies.


Algorithmia joins forces with DataRobot to deliver best-in-class MLOps in the enterprise AI platform

I’m excited to announce that Algorithmia is being acquired by DataRobot. Together, DataRobot and Algorithmia will deliver best-in-class MLOps as part of the leading, end-to-end enterprise AI platform. After seven years of watching Algorithmia grow into the leading enterprise MLOps platform, I could not be more excited to announce today that Algorithmia is being acquired by DataRobot .


Accelerating Machine Learning with MLOps and FuseML: Part One

Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.


Deep Learning Toolkit 3.6 - Automated Machine Learning, Random Cut Forests, Time Series Decomposition, and Sentiment Analysis

We’re excited to share that the Deep Learning Toolkit App for Splunk (DLTK) is now available in version 3.6 for Splunk Enterprise and Splunk Cloud. The latest release includes: Let’s get started with the new operational overview dashboard which was built using Splunk’s brand new dashboard studio functionality which I highly recommend checking out. You can learn more about it in this recent tech talk which you can watch on demand.


Operationalizing Machine Learning for the Automotive Future

It’s no secret that global mobility ecosystems are changing rapidly. Like so many other industries, automakers are experiencing massive technology-driven shifts. The automobile itself drove radical societal changes in the 20th century, and current technological shifts are again quickly restructuring the way we think about transportation. The rapid progress in AI/ML has propelled the emergence of new mobility application scenarios that were unthinkable just a few years ago.


What's new with BigQuery ML: Unsupervised anomaly detection for time series and non-time series data

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.