Machine Learning


Enabling NVIDIA GPUs to accelerate model development in Cloudera Machine Learning

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. While model training on small data can typically take minutes, doing the same on large volumes of data can take hours or even weeks. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads.

Building Automated ML Pipelines in Cloudera Machine Learning

In this video, we'll walk through an example on how you can use Cloudera Machine Learning to run some python code that creates specific Machine Learning models. We’ll then go through some features within Cloudera Machine Learning such as job scheduling and model deployments to see how you can do some more advanced machine development operations!

Splunk Machine Learning Environments (SMLE) Labs Beta Demo

Check out a demo of SMLE Labs (beta). SMLE is a purpose-built environment, bringing the power of data science and machine learning to production workloads for our Splunk customers. We support a seamless end-to-end ML journey with development, deployment, monitoring, and management — eliminating disjointed solutions with a new, streamlined experience optimized for productivity.

How to Tap into Higher-Level Abstraction, Efficiency & Automation to Simplify your AI/ML Journey

You’ve already figured out that your data science team cannot keep developing models on their laptops or a managed automated machine learning (AutoML) service and keep their models there. You want to put artificial intelligence (AI) and machine learning (ML) into action and solve real business problems.


Time-based scaling of Enterprise Search on Elastic Cloud

Does your Elastic Enterprise Search Cloud deployment follow a predictable usage pattern? You can automatically scale up and down your deployment on a schedule to achieve optimal performance and reduce operating costs. In this article we show you how to use the Elastic Cloud API to change how many Enterprise Search nodes you’re running. We call these APIs from a cron job to achieve hands-free, time-triggered autoscaling.

Algorithmia and MLflow: Integrating open-source tooling with enterprise MLOps

We’re excited to share our new integration with MLflow, a popular open-source platform for managing various stages of the ML lifecycle. With this new integration, you can build and train your models using MLflow, then deploy them to production with Algorithmia where you can use our advanced features for enterprise ML operations and governance.

In the rush to adopt machine learning, don't forget to empower human intelligence

Machine learning has gained popularity in cybersecurity, and for good reason: in the never-ending race against dwell times, security professionals are looking for an advantage. While machine learning can help reduce the mean time to detect and respond to a threat, organizations should be careful not to rely so much on machine learning that they forget to empower their most important asset: human intelligence.