As discussed in our previous post, we recently had the opportunity to present some interesting challenges and proposed directions for data science and machine learning (ML) at the 2018 Scale By the Bay conference. While the excellent talks and panels at the conference were too numerous to cover here, I wanted to briefly summarize three talks in particular that I found to represent some really interesting (to me) directions for ML on the java virtual machine (JVM).

We finally made it to another new year, and that means it’s time to reflect on the learnings from the previous year while also preparing for many new opportunities and challenges ahead. The enterprise tech and security industry didn’t seem to slow in 2018, so there’s no reason we would expect 2019 to be any different. So what will those “hot button” topics be this year?

In this latest SnapSecChat video series, our CSO, George Gerchow, talks about the imperative for building the next-gen security operations center (SOC) of the future. Why? Because today’s IT landscape is becoming increasingly complex and cloud-based.

This year, at Sumo Logic’s third annual user conference, Illuminate 2018, we presented Sumo Logic Notebooks as a way to do data science within the Sumo Logic platform. Sumo Logic Notebooks integrate Sumo Logic data, data science notebooks and common machine learning frameworks.

It finally happened. At the start of DockerCon Europe and a week before KubeCon was set to take place in the U.S., researchers discovered the first major vulnerability within Kubernetes, the popular cloud container orchestration system.

Data science and machine learning have gotten a lot of attention recently, and the ecosystem around these topics is moving fast. One significant trend has been the rise of data science notebooks (including our own here at Sumo Logic): interactive computing environments that allow individuals to rapidly explore, analyze, and prototype against datasets.