The Value of Certification

Categories: Careers Training

Each year in early November, my inbox fills up with people asking advice about certification. Some are reflecting on their careers and looking to move on or move up; others have given themselves or their managers the goal of getting certified this year. They awake one morning in early November and realize the clock is ticking.

The first thing they ask for is a discount, of course. Beyond that, they want to know what a certification is going to do for them more generally,

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New in Cloudera Data Science Workbench 1.2: Usage Monitoring for Administrators

Categories: CDH Cloudera Data Science Workbench Data Science Performance

Cloudera Data Science Workbench (CDSW) provides data science teams with a self-service platform for quickly developing machine learning workloads in their preferred language, with secure access to enterprise data and simple provisioning of compute. Individuals can request schedulable resources (e.g. compute, memory, GPUs) on a shared cluster that is managed centrally.

While self-service provisioning of resources is critical to the rapid interaction cycle of data scientists, it can pose a challenge to administrators.

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Getting Started with Cloudera’s Cybersecurity Solution

Categories: CDH How-to Platform Security & Cybersecurity

A quick conversation with most Chief Information Security Officers (CISOs) reveals they understand they need to modernize their security architecture and the correct answer is to adopt a machine learning and analytics platform as a fundamental and durable part of their data strategy. However, many CISOs fear deployment of an initial use case will be somewhat daunting. Cloudera has partnered along with Arcadia Data and StreamSets to make it easier than ever for CISOs to take the first step and deploy basic use cases leveraging data sources common to many environments.

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Deep learning with Apache MXNet on Cloudera Data Science Workbench

Categories: CDH Cloudera Data Science Workbench Data Science

With the abundance of deep learning frameworks available today, it can be difficult to know what to choose for any particular application. Given the contrasting strengths and weaknesses of these frameworks, the ability to work with and switch between more than one is particularly important. Recent Cloudera blogs have shown how examples of applying deep learning on the Cloudera ecosystem using popular frameworks Deeplearning4j, BigDL, and Keras+TensorFlow.

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