Notes about Cutting-Edge Technologies and Everything

Ray intends to be a universal framework for a wide range of machine learning applications. This includes distributed training, machine learning inference, data processing, latency-sensitive applications, and throughput-oriented applications. Each of these applications has different, and, at times, conflicting requirements for resource management. Ray intends to cater to all of them, as the newly emerging microkernel for distributed machine learning. In order to achieve that kind of generality, Ray enables explicit developer control with respect to the task and actor placement by using custom resources. In this blog post we are going to talk about use cases and provide examples. This article is intended for readers already familiar with Ray. If you are new to Ray are are looking to easily and elegantly parallelize your Python code, please take a look at this tutorial.  USE CASES Load Balancing.  In many cases, the preferred behavior is to distribute tasks across all...

The DGI Data Governance Framework   is a logical structure for classifying, organizing, and communicating complex activities involved in making decisions about and taking action on enterprise data.

Machine learning platforms are not the wave of the future. It's happening now. Developers need to know how and when to harness their power. Working within the ML landscape while using the right tools like Filestack can make it easier for developers to create a productive algorithm that taps into its power. The following machine learning platforms and tools — listed in no certain order — are available now as resources to seamlessly integrate the power of ML into daily tasks. 1.  H2O H2O was designed for the Python, R, and Java programming languages by H2O.ai. By using these familiar languages, this open source software makes it easy for developers to apply both predictive analytics and machine learning to a variety of situations. Available on Mac, Windows, and Linux operating systems, H2O provides developers with the tools they need to analyze data sets in the Apache Hadoop file systems as well as those in the cloud. 2.  Apache PredictionIO Developer...

First published in 2000, SFIA has evolved through successive updates as a result of expert input by its global users to ensure that, first and foremost, it remains relevant and useful to the needs of the industry and business.  SFIA 7, as with previous updates, is an evolution. It has been updated in response to many change requests: many of the existing skills have been updated and a few additional ones introduced but the key concepts and essential values of SFIA remain true, as they have done for nearly 20 years. The structure has remained the same – 7 levels of responsibility characterised by generic attributes, along with many professional skills and competencies described at one or more of those 7 levels.  The SFIA standard covers the full breadth of the skills and competencies related to information and communication technologies, digital transformation and software engineering. SFIA is also often applied to a range of other technical endeav...