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Validating Jet Engine Predictive Models Using Cloudera Machine Learning

In this video, we’ll go over how to use Cloudera Machine Learning (CML) to validate a complex predictive model. Using a publicly available NASA dataset that simulates how jet engines degrade over time, we’ll use machine learning concepts in a cloud environment to go from simulation data to a cost benefit analysis in just a few steps. We’ll also show how we can run experiments to track specific metrics from many different scenarios that our predictive model could possibly be implemented in.

Redivis makes research data accessible, experiences collaborative with BigQuery

Understanding the data we collect is essential—it allows us to identify trends and uncover answers about our world. However, stories in our data frequently go untold. Large datasets are hard to share between research communities due to their size, security restraints, and complexity. Even if these datasets are accessible to users, the tools needed to query them often require deep technical knowledge.

Smile with new user-friendly SQL capabilities in BigQuery

October happens to be the month to celebrate World Smile Day when Harvey Ball, the inventor of the smiley face declared this day as such to give people a reason to smile. This month, BigQuery users have a lot of new reasons to smile about with the release of new user-friendly SQL capabilities now generally available.

Using Cloudera Machine Learning to Build a Predictive Maintenance Model for Jet Engines

Running a large commercial airline requires the complex management of critical components, including fuel futures contracts, aircraft maintenance and customer expectations. Airlines, in just the U.S. alone, average about 45,000 daily flights and transporting over 10 million passengers a year (source: FAA). Airlines typically operate on very thin margins, and any schedule delay immediately angers or frustrates customers.

Apache Spark on Kubernetes: How Apache YuniKorn (Incubating) helps

Apache Spark unifies batch processing, real-time processing, stream analytics, machine learning, and interactive query in one-platform. While Apache Spark provides a lot of capabilities to support diversified use cases, it comes with additional complexity and high maintenance costs for cluster administrators. Let’s look at some of the high-level requirements for the underlying resource orchestrator to empower Spark as a one-platform.

How Software Companies Can Build Scalable Embedded Analytics Apps with Snowflake

Customers of B2B companies rely on insights from applications to grow their business, secure their infrastructure, make business decisions, and more. Unless your B2B company offers a rich set of analytics within its product, your customers likely demand nightly data dumps from your application so they can analyze application data with their own BI stack.

What you need to know to begin your journey to CDP

Recently, my colleague published a blog build on your investment by Migrating or Upgrading to CDP Data Center, which articulates great CDP Private Cloud Base features. Existing CDH and HDP customers can immediately benefit from this new functionality. This blog focuses on the process to accelerate your CDP journey to CDP Private Cloud Base for both professional services engagements and self-service upgrades.