If you are a software engineer, there's a good chance that deep learning will inevitably become part of your job in the future. Even if you're not building the models that directly use CNNs, you might have to collaborate with data scientists or help business partners better understand what is going on under the hood. In this article, Julie Kent dives into the world of convolutional neural networks and explains it all in a not-so-scary way.

We're excited to announce that the Splunk Machine Learning Toolkit (MLTK) version 5.2 is available for download today on Splunkbase! Earlier this month, I discussed how the release of version 5.2 will make machine learning more accessible to more users. Splunk’s MLTK lets our customers apply machine learning to the data they're already capturing in Splunk, develop models, and operationalize these algorithms to glean new insights and make more informed decisions.

For teams that deal with machine learning (ML), there comes a point in time where training a model on a single machine becomes untenable. This is often followed by the sudden realization that there is more to machine learning than simply model training. There are a myriad of activities that have to happen before, during and after model training. This is especially true for teams that want to productionize their ML models.

In sync with the upcoming release of Splunk’s Machine Learning Toolkit 5.2, we have launched a new release of the Deep Learning Toolkit for Splunk (DLTK) along with a brand new “golden” container image. This includes a few new and exciting algorithm examples which I will cover in part 2 of this blog post series.

In part 1 of this release blog series we introduced the latest version of the Deep Learning Toolkit 3.1 which enables you to connect to Kubernetes and OpenShift. On top of that a brand new “golden image” is available on docker hub to support even more interesting algorithms from the world of machine learning and deep learning! Over the past few months, our customers’ data scientists have asked for various new algorithms and use cases they wanted to tackle with DLTK.

As we connect with customers we increasingly hear the need for teams to be more predictive with their data. A big challenge is uncertainty around how to get started, especially when much of their data is unstructured. At Splunk, our goal is to make data — and machine learning — accessible for a broad range of users. The good news is, with machine learning doing even more work on your behalf, you don’t need to be a data scientist to use these advanced capabilities.

As online commerce has boomed, many companies now manage a large number of revenue streams from a variety of sources including micro-transactions, single purchases, and subscription plans. Now that revenue models have become much more complex and fragmented, many companies have realized that their traditional systems simply aren’t capable of the scale and granularity required for accurate revenue monitoring.

Binary classification aims to separate elements of a given dataset into two groups on the basis of some learned classification rule. It has extensive applications from security analytics, fraud detection, malware identification, and much more. Being a supervised machine learning method, binary classification relies on the presence of labeled training data that can be used as examples from which a model can learn what separates the classes.

Managing a network more effectively has been something our customers have been asking us about for many years, but it has become an increasingly important topic as working from home becomes the new normal across the globe. In this blog series, I thought I’d present a few analytical techniques that we have seen our customers deploy on their network data to: Better understand their network and Develop baselines for network behaviour and detect anomalies.

A difficult question we come across with many customers is ‘what does normal look like for my network?’. There are many reasons why monitoring for changes in network behaviour is important, with some great examples in this article - such as flagging potential security risks or predicting potential outages.