Systems | Development | Analytics | API | Testing

10 Steps to Achieve Enterprise Machine Learning Success

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. It’s a pie-in-the-sky statement that sounds great, right? And while you’d be forgiven for thinking that it might sound too good to be true, operational ML is, in fact, achievable and sustainable. You can get the very kind of ML you need to increase revenue and lower costs. To help teams work smarter and do things faster.

The Key to Unlocking IT Modernization's Power? Enterprise level Transformation

The United States Veterans Administration (VA) over the last decade underwent a massive enterprise-wide IT transformation, eliminating its fragmented shadow IT and adopting a centralized system capable of supporting the agency’s 400,000 employees and more effectively utilizing its $240 billion-plus annual budget. The result: A more reliable and modern IT environment that improves access, availability, and user experience -ultimately supporting the VA mission more effectively.

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.

Next Stop - Predicting on Data with Cloudera Machine Learning

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization.

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!

Enabling kubectl for CDE

The kubectl tool provides direct administrative access to the Kubernetes cluster underlying a CDE service, which is useful for troubleshooting, among other things. This video will demonstrate how to set up kubectl access. To enable kubectl, we will need a couple of prerequisites. We wiil need the kubeconfig file from the CDE service. We will need to get and authorize the IAM user, and then need to make sure that everything is set up correctly, both for kubectl and some other tools like k9s.