Risk management is a highly dynamic discipline these days. Stress testing is a particular area that has become even more important throughout the pandemic. Stress tests conducted by authorities such as the Federal Reserve Bank in the US are designed to keenly monitor the financial stability of the banking sector, especially during economic downturns such as those brought on by the pandemic.
In the latest installment of the EMEA Influential Women in Data webinar series, we welcomed Shirley Collie, Chief Health Analytics Actuary at Discovery Health to discuss everything from how the pandemic has impacted working, to the opportunities within data, and the importance of intentionality.
The telecommunications industry has been doing well since the pandemic started (not that many would notice). Revenues have remained relatively stable, while consumption has gone up, as virtual engagement has become the primary mode of operations for many businesses (and families!) In the mean-time, digital transformation has been accelerating both as a means to respond to the pandemic, and as a mechanism to drive costs down further, allowing for margin growth.
Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists. This unprecedented level of big data workloads hasn’t come without its fair share of challenges.
CDP Private Cloud Base is an on-premises version of Cloudera Data Platform (CDP). This new product combines the best of Cloudera Enterprise Data Hub and Hortonworks Data Platform Enterprise along with new features and enhancements across the stack. This unified distribution is a scalable and customizable platform where you can securely run many types of workloads. CDP is an easy, fast, and secure enterprise analytics and management platform with the following capabilities.
While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” classification problem. Figure 01: Is this a chihuahua or a muffin? In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin.
Note: This is part 2 of the Make the Leap New Year’s Resolution series. For part 1 please go here. When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale.
In recent years, Ethical AI has become an area of increased importance to organisations. Advances in the development and application of Machine Learning (ML) and Deep Learning (DL) algorithms, require greater care to ensure that the ethics embedded in previous rule-based systems are not lost. This has led to Ethical AI being an increasingly popular search term and the subject of many industry analyst reports and papers.
After the launch of Cloudera DataFlow for the Public Cloud (CDF-PC) on AWS a few months ago, we are thrilled to announce that CDF-PC is now generally available on Microsoft Azure, allowing NiFi users on Azure to run their data flows in a cloud-native runtime. With CDF-PC, NiFi users can import their existing data flows into a central catalog from where they can be deployed to a Kubernetes based runtime through a simple flow deployment wizard or with a single CLI command.
Cloudera has been recognized as a Visionary in 2021 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS) and for the first time, evaluated CDP Operational Database (COD) against the 12 critical capabilities for Operational Databases.
