Implementing distributed model training for deep learning with Cloudera Machine Learning

Many enterprise data science teams are using Cloudera’s machine learning platform for model exploration and training, including the creation of deep learning models using Tensorflow, PyTorch, and more. However, training a deep learning model is often a time-consuming process, thus GPU and distributed model training approaches are employed to accelerate the training speed.


What's New in the Splunk Machine Learning Toolkit 5.2?

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.


Introduction to Machine Learning Pipelines with Kubeflow

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.


How AI is Transforming the Way Customer Service Teams Work in the 2020s

AI was once a concept that belonged in the realm of science fiction. There was even a major Hollywood film with that exact two-letter title. It may have been novel then, but as we move into the 2020s, things are very different indeed. Artificial intelligence – a term used to describe a group of technologies – is having an immense impact on everyday life. AI is reshaping processes and activities in a wide range of settings.


Deep Learning Toolkit 3.1 - Release for Kubernetes and OpenShift

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.


Deep Learning Toolkit 3.1 - Examples for Prophet, Graphs, GPUs and DASK

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.


Making Machine Learning Accessible to More Users

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.


Monitoring Micro-Transaction Payment Models with AI

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.


Benchmarking binary classification results in Elastic machine learning

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.


Machine learning in production: Human error is inevitable, here's how to prepare.

You did it. You have machine learning capabilities up and running in your organization. Success! What started as a few nascent experiments (and maybe a few failures) are now carefully constructed models racing along in full production—with the ability to scale into the hundreds or thousands of productional models in sight. Assembling your expert team of data scientists and custodians seems like a distant memory. Now you’re looking ahead to the future—growth, innovation, revenue!