Stephen Elston

Stephen F. Elston, managing director of Quantia Analytics, LLC, is a big data geek and data scientist, with more than two decades of experience with predictive analytics, machine learning, and R and S/SPLUS. He leads architecture, development, sales and support for predictive analytics and machine learning solutions. Steve holds a PhD degree in Geophysics from Princeton University. Formerly, he led R&D for the S-PLUS companies and is a cofounder of FinAnalytica, Inc.

Get started with cloud-based data science

Learn how to deploy machine learning solutions using Azure ML.


Download the free, updated report “Data Science in the Cloud with Microsoft Azure Machine Learning and R: 2015 Update.

Cloud-based machine learning platforms, like Microsoft’s Azure Machine Learning (Azure ML), provide a simplified path to create and deploy analytic solutions. Azure ML is a fully managed and secure machine learning platform that resides within the Microsoft Cortana Analytics Suite.

Azure ML workflows (known as “experiments”) are constructed using a combination of drag-and-drop modules, SQL, R, and Python scripts. The wide range of built modules support the typical steps in a machine learning workflow, from data ingestion and data munging to model construction and cross validation.

Once your Azure ML experiment is ready, there are several options to deploy it. Azure ML experiments can access large-scale data stored in Azure Blob storage, Azure SQL and Hive, to name a few options. Similarly, your experiment can write results back to multiple scalable Azure storage options.

Read more…

Getting started with data science in the cloud

Learn how to manipulate data, and construct and evaluate models in Azure ML, using a complete data science example.

Large-scale machine learning, or predictive analytics, is having a powerful impact across many industries. By using machine learning, companies, governments, and not-for-profits are replacing guesses and seat-of-the-pants estimates with valuable data-driven predictions.

Deriving value from machine learning, however, is often impeded by complex technology deployments and long model-development cycles. Fortunately, machine learning and data science are undergoing democratization. Workflow environments make tools for building and evaluating sophisticated machine learning models accessible to a wider range of users. Cloud-based environments provide secure ubiquitous access to data storage and powerful data science tools.

To get you started creating and evaluating your own machine learning models, O’Reilly has commissioned a new report: “Data Science in the Cloud, with Azure Machine Learning and R.” We use an in-depth data science example — predicting bicycle rental demand — to show you how to perform basic data science tasks, including data management, data transformation, machine learning, and model evaluation in the Microsoft Azure Machine Learning cloud environment. Using a free-tier Azure ML account, example R scripts, and the data provided, the report provides hands-on experience with this practical data science example. Read more…