How to make Python easier for the R user: revoscalepy

by Siddarth Ramesh, Data Scientist, Microsoft I’m an R programmer. To me, R has been great for data exploration, transformation, statistical modeling, and visualizations. However, there is a huge community of Data Scientists and Analysts who turn to Python for these tasks. Moreover, both R and Python experts exist in most analytics organizations, and it is important for both languages to coexist. Many times, this means that R coders will develop a workflow in R but then must redesign and recode it in Python for their production systems. If the coder is lucky, this is easy, and the R model can be exported as a serialized object and read into Python. There are packages that do this, such as pmml. Unfortunately, many times, this is more challenging because the production system might demand that the entire end to end workflow is built…
Original Post: How to make Python easier for the R user: revoscalepy

Scale up your parallel R workloads with containers and doAzureParallel

by JS Tan (Program Manager, Microsoft) The R language is by and far the most popular statistical language, and has seen massive adoption in both academia and industry. In our new data-centric economy, the models and algorithms that data scientists build in R are not just being used for research and experimentation. They are now also being deployed into production environments, and directly into products themselves. However, taking your workload in R and deploying it at production capacity, and at scale, is no trivial matter.  Because of R’s rich and robust package ecosystem, and the many versions of R, reproducing the environment of your local machine in a production setting can be challenging. Let alone ensuring your model’s reproducibility! This is why using containers is extremely important when it comes to operationalizing your R workloads. I’m happy to announce that…
Original Post: Scale up your parallel R workloads with containers and doAzureParallel

Highlights from the Connect(); conference

Connect();, the annual Microsoft developer conference, is wrapping up now in New York. The conference was the venue for a number of major announcements and talks. Here are some highlights related to data science, machine learning, and artificial intelligence: Lastly, I wanted to share this video presented at the conference from Stack Overflow. Keep an eye out for R community luminary David Robinson programming in R! You can find more from the Connect conference, including on-demand replays of the talks and keynotes, at the link below. Microsoft: Connect(); November 15-17, 2017
Original Post: Highlights from the Connect(); conference

Developing AI applications on Azure: learning plans at three levels

If you’re looking to expand your skills as an AI developer, or just getting started, these learning plans for AI Developers on Azure provide a wealth of information to get you up to speed. The beginner, intermediate and advanced tracks all provide step-by-step guides to setting up the tools and data in Azure, along with worked examples in iPython Notebooks. The Beginner AI Developer Learning Plan provides an introduction to artificial intelligence and cognitive systems. It begins with an overview of Cognitive Services, and then works through several examples of using those APIs in applications: handwriting comprehension, speech comprehension, and face detection. The Intermediate AI Developer Learning Plan walks through the process of creating an AI application that understands voice input. It begins with an overview of LUIS, the Language Understanding Intelligent Service, and walks through the process of defining intents, entities,…
Original Post: Developing AI applications on Azure: learning plans at three levels

Recap: EARL Boston 2017

By Emmanuel Awa, Francesca Lazzeri and Jaya Mathew, data scientists at Microsoft A few of us got to attend EARL conference in Boston last week which brought together a group of talented users of R from academia and industry. The conference highlighted various Enterprise Applications of R. Despite being a small conference, the quality of the talks were great and showcased various innovative ways in using some of the newer packages available for use in the R language. Some of the attendees were veteran R users while some were new comers to the R language, so there was a mix in the level of proficiency in using the R language.   R currently has a vibrant community of users and there are over 11,000 open source packages. The conference also encouraged women to join their local chapter for R Ladies…
Original Post: Recap: EARL Boston 2017

Microsoft R Open 3.4.2 now available

Microsoft R Open (MRO), Microsoft’s enhanced distribution of open source R, has been upgraded to version 3.4.2 and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to the latest R 3.4.2 and updates the bundled packages.  MRO is 100% compatible with all R packages. MRO 3.4.2 points to a fixed CRAN snapshot taken on October 15 2017, and you can see some highlights of new packages released since the prior version of MRO on the Spotlights page. As always you can use the built-in checkpoint package to access packages from an earlier date (for compatibility) or a later date (to access new and updated packages). MRO 3.4.2 is based on R 3.4.2, a minor update to the R engine (you can see the detailed list of updates to R here). This update is backwards-compatible with…
Original Post: Microsoft R Open 3.4.2 now available

Two upcoming webinars

Two new Microsoft webinars are taking place over the next week that may be of interest: AI Development in Azure using Data Science Virtual Machines The Azure Data Science Virtual Machine (DSVM) provides a comprehensive development and production environment to Data Scientists and AI-savvy developers. DSVMs are specialized virtual machine images that have been curated, configured, tested and heavily used by Microsoft engineers and data scientists. DSVM is an integral part of the Microsoft AI Platform and is available for customers to use through the Microsoft Azure cloud. In this session, we will first introduce DSVM, familiarize attendees with the product, including our newest offering, namely Deep Learning Virtual Machines (DLVMs). That will be followed by technical deep-dives into samples of end-to-end AI development and deployment scenarios that involve deep learning. We will also cover scenarios involving cloud based scale-out and…
Original Post: Two upcoming webinars