Tags : #AzureML

25

May

Deployment of Pre-Trained Models on Azure Container Services
Deployment of Pre-Trained Models on Azure Container Services

This post is authored by Mathew Salvaris, Ilia Karmanov and Jaya Mathew.

Data scientists and engineers routinely encounter issues when moving their final functional software and code from their development environment (laptop, desktop) to a test environment, or from a staging environment to production. These difficulties primarily stem from differences between the underlying software environments and infrastructure, and they eventually end up costing businesses a lot of time and money, as data scientists and engineers work towards narrowing down these incompatibilities and either modify software or update environments to meet their needs.

Containers end up being a great solution in such scenarios, as the entire runtime environment (application, libraries, binaries and other configuration files) get bundled into a package to ensure smooth portability of software across different environments. Using containers can, therefore, improve the speed at which apps can be developed, tested, deployed and shared among users working in different environments. Docker is a leading software container platform for enabling developers, operators and enterprises to overcome their application portability issue.

The goal of Azure Container Services (ACS) is to provide a container hosting environment by using popular open-source tools and technologies. Like all software, deploying machine learning (ML) models can

23

May

“Serving AI with Data” – Recap of the Microsoft AI Immersion Workshop

Artificial Intelligence is enjoying its day in the sun. Articles about AI get regularly featured as front page news, and in a wide variety of applications, including conversational bots, autonomous vehicles, connected machines, medical diagnosis, and even better-than-human speech/image recognition, it seems. AI is increasing interest to millions of developers worldwide, as well as companies of all stripes, from garage startups to the largest enterprises.

Microsoft has a long-term commitment to the field of AI. We are partnering with developers and customers as we work towards our dream, which is to democratize AI and its benefits to every person and every organization. AI was a central theme at Microsoft Build earlier this month, where, based on feedback received from attendees and in media reports such as this one, it appears that the community appreciates our AI vision and roadmap.

But it’s still very early days for AI.

A majority of developers are still ramping up, wrapping their heads around this new slew of technologies and accompanying TLAs. Realizing this to be the case, we decided to host a first-of-its-kind Microsoft AI Immersion Workshop earlier this month – a free pre-event to Build 2017, with the goal of giving

16

May

Democratizing AI Through Microsoft Certifications in Data Science & Machine Learning

Posted by Marla Michel, Senior Program Manager for Ecosystem Development & Training at Microsoft.

This blog is the first in a series where we will discuss several new certification exams for data professionals and the resources to prepare for them.

When individuals decide which certification to take, their decision is typically based on their role and – in this particular domain – their relationship with data and analytics. Common role or titles we encounter in our domain include data scientists, data analysts, data engineers, data architects and data developers, although by no means is this list exhaustive. What’s more, with the democratization of artificial intelligence, it is inevitable that almost all current and future data professionals will be using, or will need to be educated about, machine learning and artificial intelligence, and many will want to have certifications to prove their skills.

The Cortana Intelligence Suite is used today by many Microsoft customers who are building end-to-end business solutions, applying the very latest in ML and AI techniques on their big data. Indeed, this powerful set of technologies is all set to drive the next stage in our customers’ digital transformation. Companies and individuals around the world are

11

May

Using IoT Sensors to Up Your Game
Using IoT Sensors to Up Your Game

This post is authored by Patty Ryan, Principal Data Scientist, Hang Zhang, Senior Data and Applied Scientist, and Mustafa Kasap, Senior Software Design Engineer, at Microsoft.

Learn from the Professionals through Comparison of Sensor Data

As any athlete aspiring to greatness can tell you, measurement of your own performance and tips from the pros are two keys to improvement. Thanks to affordable wearable sensors, it is now possible for you to measure your own performance and also benchmark it to that of the professionals.

Everyone knows that a professional practicing a sport looks visibly different from an amateur. In skiing, we’ve identified just nine sensor positions that can clearly differentiate professionals from the amateurs. The information from these nine sensors allowed us to build a simple but powerful machine learning model that can classify professionals and non-professionals correctly 98% of the time.

Sensor Data Delivers an Activity Proficiency Signature

Here’s how it works: Each of the sensors measure position, acceleration and rotation individually, and record this data along with a time stamp. Sensor data includes position, acceleration and rotation, all relative to x, y and z coordinates. While the sample rate of sensors varies, we recommend a minimum

10

May

Now Serving: More AI with Your Big Data
Now Serving: More AI with Your Big Data

Re-posted from the Microsoft SQL Server blog.

Earlier today, at Build 2017, we made a string of announcements to further help developers and customers around the planet create breakthrough experiences through the power of artificial intelligence and big data. There were 3 major themes to these announcements:

1. AI at the Heart of the Microsoft Data Platform

Microsoft is simplifying the deployment of AI-powered apps by bringing intelligence into existing data platforms. The extensibility of our database architecture helped us introduce R support to SQL Server 2016, and we’re now adding Python support in our upcoming SQL Server 2017 release. Developers can tap into GPU-accelerated computing through the Python/R interfaces in SQL Server, implementing sophisticated AI directly in the database and gaining massively higher throughput on even their most intensive deep learning jobs, including on images and other unstructured data.

We are delivering key SQL Server 2017 enhancements to Azure SQL Database, giving you a consistent programming surface across on-premises and cloud. Today, we announced that support for Graph is coming to Azure SQL Database. Additionally, Azure SQL Database uses AI within the service itself, learning from your unique patterns, making performance and tuning recommendations, and even taking automatic

08

May

Optimizing Intelligent Apps Using Machine Learning in SQL Server

Re-posted from the Azure blog.

SQL Server 2016 introduced a new function called R Services, bringing machine learning capabilities to the platform, and the next version of SQL Server will further extend this with support for Python. These new capabilities – of running R and Python in-database and at scale – let developers keep their analytics services close to the data, eliminating expensive data movements. They also simplify the process of developing and deploying a new breed of intelligent apps.

There are several optimization tips and tricks available to developers, to help you get the most mileage out of SQL Server in these scenarios, including fine-tuning the model and boosting performance. In a new blog post, we apply some of these optimization techniques to a resume-matching scenario and demonstrate how such techniques can make your data analytics much more efficient and powerful. The optimization techniques covered are:

Full durable memory-optimized tables.
CPU affinity and memory allocation.
Resource governance and concurrent execution.

We ran benchmark tests where we compared the scoring time with and without these optimizations, scoring 1.1 million rows of data using the RevoScaleR and MicrosoftML packages to separately train a prediction model. Tests were run on the same

08

May

Microsoft’s Chris Bishop Elected as a Fellow of the Royal Society

Re-posted from the Microsoft Next blog.

Chris Bishop, distinguished scientist, director of Microsoft Research Cambridge (UK), and a renowned expert in artificial intelligence and machine learning, was elected a Fellow of the Royal Society, the oldest scientific academy in continuous existence.

The addition of Chris to this elite body talks to the preeminence of AI and ML in scientific work and indeed in all human endeavor.

Bishop was fascinated by science and technology at an early age and his interest in AI was sparked by Hal, the sentient computer from “2001: A Space Odyssey”. He joined Microsoft Research Cambridge in 1997 and was named lab director in 2015. His current projects include collaborations with medical specialists to develop new applications of ML, including a project with the University of Manchester to understand factors that influence the development of allergies and asthma in children. Bishop has won multiple awards and honors for his research achievements and his broader contributions to the field of science. He also holds a chair in computer science at the University of Edinburgh.

Chris is no stranger to this blog, having written about probabilistic inference and also having contributed to or reviewed other posts.

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…