As we kick off what will surely be another very exciting year of progress in artificial intelligence, machine learning and data science, we start with a quick recap of our “Top 10” most popular posts (based on aggregate readership) from the year just concluded.
Here are the posts that had the most page views in the recently concluded year, in increasing order of readership:
We announced a major set of updates to the Data Science VM (DSVM) in September last year. DSVM gives you a comprehensive set of tools for data movement, storage, exploration/visualization, modeling with ML/AI algorithms, and operationalization – and using multiple languages in either Linux or Windows environments.
Just last month, we announced our latest and most powerful version of Microsoft R Server. Supporting popular operating systems and a variety of data sources, MRS 9.0 helps you create and deploy sophisticated analytics models for real world problems, efficiently and at scale.
An unexpected post for a Top 10 list, perhaps, but this goes to show the broad excitement in our community around the possibilities of computer vision, deep learning and IoT. Our award-winning vision and deep learning capabilities around image processing are now a cornerstone of an ever-widening array of products offered both by Microsoft directly and our customers and partners.
In October, we introduced the Team Data Science Process – a methodology and set of practices designed to help your business truly reap the benefits promised by collaborative data science.
Back in March last year, we gamified Cortana Intelligence with the launch of our competition platform, and announced our first ever contest, a very successful competition on Decoding Brain Signals.
A link to a webinar we delivered early last year, in response to an ask from the community on what it takes to be a successful data scientist. Both the webinar and blog post emphasize the importance of building things first-hand.
This was the first in a series of posts we launched in September last year, showcasing deep learning workflows on Azure. In this post, we setup N-Series VMs on Azure with NVIDIA CUDA and cuDNN support, using MXNet as one example of deep learning frameworks that can run on Azure. We also show how Microsoft R Server can harness the deep learning capabilities of MXNet and Azure GPUs using simple R scripts.
Few things in life can beat “free”, and that was certainly true about our free eBook on creating intelligent apps using SQL Server and R. You can now embed intelligent analytics and data transformations right in your database, and make transactions intelligent in real time. Combining the performance of SQL Server in-memory OLTP and in-memory columnstores with R and machine learning, apps can achieve extraordinary analytical performance in production – and with all the benefits you expect from our industrial-strength database, including high throughput, parallelism, security, reliability, compliance certifications and great manageability.
The ML and data science community was just as delighted as us when we announced the newest language spoken by Visual Studio, back in March 2016.
Early in 2016, we announced how we are delivering Microsoft R Server across multiple platforms, allowing enterprise customers to standardize advanced analytics on one core tool, and regardless of whether they are using Hadoop, Linux or Teradata. We also announced that, on Windows, Microsoft R Server (MRS) would be included in SQL Server 2016. This post also talked about the free Developer Edition of MRS, as well as our commitment to Microsoft R Open. This was our #1 most widely read and circulated post of 2016.
Stay tuned to this channel for much more exciting news in 2017.
We wish all our readers a very happy and prosperous new year.
CIML Blog Team