Tag: datasets

Feed: Google Cloud Platform Blog. Author: GCP Team. By Venkat Krishnamurthy, Vice President, Product, MapD Editor’s note: Today, we hear from our partner MapD, whose data analytics platform uses GPUs to accelerate queries and visualizations. Read on to learn how MapD and Google Cloud are working together. MapD and public cloud are a great fit. Combining cloud-based GPU infrastructure with MapD’s performance, interactivity and operational ease of use is a big win for our customers, allowing data scientists and analysts to visually explore billion-row datasets with fluidity and minimal hassle. Our Community and Enterprise Edition images are available on AWS, ... Read More

Feed: AWS Government, Education, & Nonprofits Blog. Author: publicsector. Cities worldwide are improving citizen services and economic and environmental outcomes through technology, while also seeking to create a vision of what a smart, connected, and sustainable city of the future will look like. While you can start building and experimenting today, it’s important to have a long-term vision and a set of principles to guide you in building cost-effective, adaptable, secure, and low-risk solutions during a time of rapid technological change.Some starting points to consider include: Cities collect an increasing amount of data. In order to integrate data with other ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Igor Bobriakov. Spark’s primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). It is a fault-tolerant collection of elements which allows parallel operations upon itself. RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs.  Creating RDD  Spark provides two ways to create RDDs: loading an external dataset and parallelizing a collection in your driver program. The simplest way to create RDDs is to take an existing collection in your program and pass it to SparkContext’s parallelize() method. For example, in ... Read More

Feed: R-bloggers. Author: Bernardo Lares. To automate the process of modeling selection and evaluate the results with visualization, I have created some functions into my personal library and today I’m sharing the codes with you. I run them to evaluate and compare Machine Learning models as fast and easily as possible. Currently, they are designed to evaluate binary classification models results. Before we start, let me show you the final outcome so you know what we are trying to achieve here with just a simple R function: So, let’s start! The results object First of all, we need to have ... Read More

Feed: What's New. Till now, processing batches of data for non-real time inferencing needed to be done by resizing large datasets into smaller chunks of data and managing real-time endpoints. With the new Batch Transform capability, customers can process batch jobs within Amazon SageMaker, through a simple API call irrespective of the data set sizes. Batch Transform jobs can be done on a range of data sets, from petabytes of data to very small data sets. SageMaker manages the provisioning of resources at the start of the job and releases them when the job is complete. The output of the ... Read More

Feed: What's New. This capability provides faster start times for training jobs, better throughput, and lower disk space usage, thus lowering model training costs even further on Amazon SageMaker. As an example, in our internal benchmarks conducted earlier this year when we launched Pipe Input Mode for Amazon Sagemaker’s built-in algorithms, start times reduced by up to 87% on 78GB training dataset, with throughput twice as fast in some benchmarks, resulting in up to 35% reduction in total training time. Prior to Pipe Input Mode, data was loaded from Amazon S3 to the Amazon Elastic Block Store (Amazon EBS) volumes ... Read More

Feed: Microsoft Azure Blog. Author: Vinod Kurpad. As your cloud spend starts to increase, being able to accurately forecast usage becomes a critical part of your plan. As the first step in enabling a forward looking view to your spend on a subscription we’re launching a forecast API. The forecast API today supports a daily or monthly grain forecast at a 95 percent confidence interval. The API also returns the last two months of actual usage that should help in looking at any trending scenarios. For future iterations of the API, we plan to support multiple other scopes up and ... Read More

Feed: Microsoft Azure Blog. Author: Pranav Rastogi. The recent announcement of Azure Data Lake Storage Gen2 preview and its support on Azure HDInsight is already leading partners to innovate at a global scale. WANdisco enables globally replicated data lakes on Azure for analytics over the freshest data. This blog explains how. The modern business landscape is ruled by data. Analytics and AI are now essential for driving key business transformation. Customers have benefited tremendously from the performance, flexibility, and low cost offered by Azure for analytics and AI workloads. Hybrid made easy with a single-click! For many organizations, however, the ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kyle Weller. Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, you can have your R/Python code execute within your database. You can work in Jupyter Notebooks, RStudio, PyCharm, VSCode, Visual Studio, wherever you want, and then send function execution to SQL Server bringing intelligence to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Tetiana Boichenko. Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let’s see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. Definitions We can define machine learning (ML) as a subset of data science that uses ... Read More