Tag: performance

Feed: Featured Blog Posts - Data Science Central. Author: Kateryna Lytvynova. The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons — machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that’s why we interviewed data science practitioners — gurus, really —regarding the useful tools they choose for theirprojects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups ... Read More

Feed: Blue Medora. Author: Mike Kelly. This article originally appeared on InfoWorld Enterprises are embracing cloud platforms, often having more than one provider to best suit the given application. But how to monitor across this hybrid environment? Most IT professionals would agree that cloud computing is more about a paradigm than a physical displacement of computing resources. The “utility” paradigm is transformational by virtue of on-demand, elastic resources. But unlike being stuck with just a single local cable provider, the range of choice in cloud computing options also provides selectivity and customization best suited for the delivery of individual applications ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Saumitra Buragohain. We’re excited to announce the long-awaited release of Hortonworks Data Platform 3.0.0. HDP 3.0 is faster, smarter, hybrid, bigger, trusted and real-time database. We encourage you to read our blog that announced HDP 3.0. You can also view our keynote presentation from DataWorks Summit, where we demoed an autonomous car (1/10 scale), trained with HDP 3.0 technologies. We also want to thank  our current and prospective customers and partners for signing up for the HDP 3.0 Early Access and we are encouraged by the huge interest- now, you can use the HDP ... Read More

Feed: Planet MySQL; Author: MySQL Performance Blog; Vadim Tkachenko | July 13, 2018 |  Posted In: Benchmarks, MySQL, MySQL 8.0, Percona Server for MySQL PREVIOUS POST Recently, Dimitri published the results of measuring MySQL 8.0 on Intel Optane storage device. In this blog post, I wanted to look at this in more detail and explore the performance of MySQL 8, MySQL 5.7 and Percona Server for MySQL using a similar set up. The Intel Optane is a very capable device, so I was puzzled that Dimitri chose MySQL options that are either not safe or not recommended for production workloads. Since we have an ... Read More

Feed: What's New. Amazon SageMaker includes hosted Jupyter notebooks that make it easy to explore and visualize your training data. To help select your machine learning (ML) algorithm, Amazon SageMaker comes with the most common ML algorithms that are pre-installed and performance-optimized. Training models is quick and easy, using a set of built-in high-performance algorithms, pre-built deep learning frameworks, or using your own framework. To make the training even faster and easier, Amazon SageMaker can automatically tune your model to achieve the highest possible accuracy. Once your model is trained and tuned, it is easy to deploy in production so ... Read More

Feed: Planet MySQL; Author: Severalnines; Before you attempt to perform any schema changes on your production databases, you should make sure that you have a rock solid rollback plan; and that your change procedure has been successfully tested and validated in a separate environment. At the same time, it’s your responsibility to make sure that the change causes none or the least possible impact acceptable to the business. It’s definitely not an easy task. In this article, we will take a look at how to perform database changes on MySQL and MariaDB in a controlled way. We will talk about ... Read More

Feed: R-bloggers. Author: Dr. Shirin Glander. I’ll be giving talks and workshops at the following three upcoming conferences; hope to meet some of you there! Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria. Why a model makes the predictions it makes, however, is generally neglected. But being able to understand and interpret such models can be immensely important for improving model quality, ... Read More

Feed: R-bloggers. Author: Markus Berroth. Machine Learning (ML) is still an underdog in the field of economics. However, it gets more and more recognition in the recent years. One reason for being an underdog is, that in economics and other social sciences one is not only interested in predicting but also in making causal inference. Thus many "off-the-shelf" ML algorithms are solving a fundamentally different problem. We here at STATWORX are also facing a variety of problems e.g. dynamic pricing optimization. "Prediction by itself is only occasionally sufficient. The post office is happy with any method that predicts correct addresses ... Read More

Feed: Actian. Author: Nick Vancas. The usefulness of an analytic database is closely tied to its ability to ingest, store, and process vast quantities of data. Data is typically ingested from multiple sources such as operational databases, CSV files, and continuous data streams. In most cases, daily data loads are measured in tens or hundreds of millions of rows, so the traditional SQL INSERT mechanism is not well suited for these data volumes.In this blog post we’ll look at Actian Vector, our columnar database ideally suited for analytic applications, and evaluate a variety of CSV data ingestion options that operate ... Read More

Feed: SAS Blogs. Author: Peter Plochan. Dass das Management von Modellrisiken eine umfassende Perspektive erfordert, habe ich in Teil 1 meiner Serie "Modellrisiko – worum geht’s dabei eigentlich?" beschrieben. Diese folgenden fünf Prinzipien sind grundlegend für den Erfolg beim Design und bei der Implementierung eines Model Risk Management Framework: Abbildung 1: Komponenten eines Model Risk Management Framework 1. Decken Sie das gesamte Spektrum in der Modellbildung ab, aber priorisieren Sie dabei Beziehen Sie alle Modelle und alle Prozesse im Modelllebenszyklus in Ihre Aktivitäten ein, die für das Thema Modellrisiko relevant sind. Nutzen Sie diesen ganzheitlichen Blick, um die wichtigsten Bereiche ... Read More