Machine learning pipelines have evolved tremendously in the past several years. With a wide variety of tools and frameworks out there to simplify building, training, and deployment, the turnaround time on machine learning model development has improved drastically. However, even with all these simplifications, there is still a steep learning curve associated with a lot of these tools. In order to use machine learning in the Elastic Stack, all you really need is for your data to be stored in Elasticsearch. Once there, extracting valuable insights from your data is as simple as clicking a few buttons in Kibana.

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.

Machine Learning helps to automate analysis and surface insights that are important for the day-to-day operation of certain business functions. In this talk, Elvis Saravia, Education Architect at Elastic, will focus on introducing a series of machine learning jobs, via the Machine Learning UI, that are easy to compose and can help classify new information (in the form of document classification) and help reveal an abnormal behavior in the data (in the form of anomaly detection).

Elastic is a search company with a simple goal: to solve the world's data problems with products that delight and inspire. As the creators of the Elastic Stack, we help thousands of organizations including Cisco, eBay, Grab, Goldman Sachs, ING, Microsoft, NASA, The New York Times, Wikipedia, and many more use Elastic to power mission-critical systems. From stock quotes to Twitter streams, Apache logs to WordPress blogs, our products are extending what's possible with data, delivering on the promise that good things come from connecting the dots. We have a distributed team of Elasticians across 30 countries (and counting), and our diverse open source community spans over 100 countries. We are looking to add a hardworking software engineer to join our team, who can help contribute to our information retrieval initiatives through machine learning (ML) and natural language processing (NLP). Your primary focus will be driving forward development of machine learning based information retrieval features in the Elastic Stack.

Organizations rely on the Elastic Stack to power their security operations because of its speed, scale, and relevance. By adopting Elastic security solutions within your SOC, your team can be equipped with the technology trusted by security teams everywhere. Three topics that will be discussed are: collect at scale, monitor your attack surface, and explore anomalies with machine learning. Matthew Isett works at Elastic as a Principal Solutions Architect. He has 15 years of software engineering experience covering distributed computing, Data Science and architectural design.

Search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more. Elasticsearch 7 is a powerful tool not only for powering search on big websites, but also for analyzing big data sets in a matter of milliseconds! It's an increasingly popular technology, and a valuable skill to have in today's job market. This comprehensive course covers it all, from installation to operations, with over 90 lectures including 8 hours of video.

While Alibaba Cloud maintains its public cloud hold in the Chinese market, the cloud provider is making moves to expand its presence across Southeast Asia. Yesterday at the Alibaba Cloud Summit Infinity 2018 in Singapore -- its first cloud computing conference outside of China -- the company launched nine products for its global market. It also expanded partnerships and debuted a partner program. The mix of launched products span from an IoT platform and tool kit, a serverless service, a machine learning platform, a security software scanning service, an online backup service, and a dedicated hosting service. All of which Alibaba will be introducing to its global market.

Shay Banon: When I first released Elasticsearch, I had one goal. Make it simple for a developer to get started, download, and install Elasticsearch on their laptop, load data into it, and get really fast results in milliseconds or less. Today we have more than 130 million downloads of our software and our community has grown to more than 100,000 developers across 100 countries. While there are lots of individual milestones for Elasticsearch, I'll highlight a few company milestones that make us who we are today. Early on in 2013, Kibana and Logstash joined forces with Elasticsearch to create the de facto open source logging solution.

Today we're proud to announce the first release of machine learning features for the Elastic Stack, available via X-Pack. Joining Elastic has been like jumping on a rocket ship, but after 7 crazy months we are excited that the Prelert machine learning technology is now fully integrated into the Elastic Stack, and we are really excited about getting feedback from users. Note: Before you get too excited, keep in mind that this functionality is marked beta in version 5.4.0. Our goal is to empower users with tools to get value and insights from their Elasticsearch data, and we view machine learning as a natural extension to the search and analytics capabilities in Elasticsearch. For example, Elasticsearch allows you to search for transactions for user'steve' in real time across huge volumes of data, or use aggregations and visualisations to show the top ten selling products or trends in transactions over time. Now, with machine learning you can go deeper and ask questions like "Have any of my services changed behaviour?"

I am happy to announce that Prelert and Elastic are joining forces. Ever since we started Elastic, our goal has been to allow users to easily find relevant data or insights within large amounts of data. Search is a wonderful way to do it, and the ability to slice, dice, and aggregate the data in an unconstrained way allowed users to feel they are in control of the data, compared to the other way around. But we can take it a step forward, and with Prelert, we just did. Prelert has developed an unsupervised machine learning engine that can plow through large amounts of data and automatically find those insights our users today have been proactively finding using search.