This post is authored by Erika Menezes, Software Engineer at Microsoft.
In Part 1 of this blog series, we created a recipe prediction model to predict recipes from a text input that may contain an arbitrary number of emojis. In this post we will go over how to operationalize this model as a web service that will be exposed as a REST API. We will be using Visual Studio Code Tools for AI to do this. We will also show you recommended practices for operationalizing large sized models and ways to troubleshoot your operationalization workflow. We present a step-by-step walkthrough, right from setting up your Azure Machine Learning account to exposing your ML model through a web endpoint.
To complete this tutorial, you need:
An Azure subscription. If you don’t have an Azure subscription, create a free account before you begin.
An experimentation account and Azure Machine Learning Workbench installed as described in this quickstart.
The classification model from Part 1.
A Docker engine installed and running locally. Learn more here.
Getting Started with Azure Machine Learning
Azure Machine Learning (AML) provides data scientists with a tool set to help you experiment and deploy faster.
We continue to expand the Azure Marketplace ecosystem. From May 1 to 15, 28 new offers successfully met the onboarding criteria and went live. See details of the new offers below:
Altova Server Platform: This free Azure virtual machine template makes it easier and more convenient to use Altova server software in the cloud. The VM template installs the complete line of Altova server software products, including the free LicenseServer, on the VM you specify in Azure.
Apptimized Test: Apptimized Test takes away the pain from constant retesting against the Windows platform. Using our unique Azure-based solution, test all your products against every Insider Release of every Microsoft Windows change well before that change moves into production.
Apptimized Catalogue: Get instant access to latest versions of the world’s most commonly packaged applications, already packaged to Apptimized’s high quality standards. No longer pay each time an application needs to be repackaged; simply log in and download the latest version.
Apptimized Packaging Service: We package applications for all formats, against any platform, without you needing to invest a penny in hardware, software, or expensive resources. Built on over 15 years delivering
https://powerbi.microsoft.com/en-us/blog/6-7-webinar-building-accessible-power-bi-reports-by-meagan-longoria/Source: https://powerbi.microsoft.com/en-us/blog/6-7-webinar-building-accessible-power-bi-reports-by-meagan-longoria/ Learn how to build inclusive and accessible Power BI reports by one of the Power BI MVPs, Meagan Longoria
The confluence of cloud, data, and AI is foundational to innovation and is driving unprecedented change. This week at Spark + AI Summit, I talked about how Microsoft enables organizations to take advantage of Azure to build advanced machine learning models and intelligent applications virtually anywhere.
As Satya mentioned during our Build conference last month, applications will increasingly require a ubiquitous computing fabric from the cloud to the edge. These applications also require new machine learning and AI capabilities that enable them to see, hear and predict. The driving force behind these capabilities is data. Data is vital to every app and experience we build today. Organizations are using their data to extract important insights to drive their businesses forward and engage their customers in new ways. Customers like Renault-Nissan are revolutionizing their customer experience with connected cars. Rockwell Automation, a leader in industrial automation has built predictive maintenance capabilities on their equipment to save time and reduce cost associated with device failure. Liebherr, a leader in manufacturing, produces intelligent refrigerators that use object recognition to recommend grocery lists based on refrigerator contents. These are just a few examples of customers leveraging their data, wherever it exists, to turn it
Azure Data Lake Analytics combines declarative and imperative concepts in the form of a new language called U-SQL. The idea of learning a new language is daunting. Don’t worry! U-SQL is easy to learn. You can learn the vast majority of the language in a single day. If you are familiar with SQL or languages like C# or Java, you will find that learning U-SQL is natural and that you will be productive incredibly fast.
A common question we get is “How can I get started with U-SQL?” This blog will show you all the core steps you need to get ramped up on U-SQL.
What is U-SQL?
U-SQL is the big data query language and execution framework in the Azure Data Lake Analytics. U-SQL uses familiar SQL concepts and language to scale out your custom code (.NET/C#/Python) from Gigabyte to Petabyte scale. U-SQL offers the usual big data processing concepts such as “schema on reads,” custom processors, and reducers. The language lets you query and combine data from multiple data sources including Azure Data Lake Storage, Azure Blob Storage, Azure SQL DB, Azure SQL Data Warehouse and SQL Server instances running on Azure VMs.
Step 1: Read the
Recently a customer asked me how to read blob data produced from the routing capability of Azure IoT Hub. To provide this customer with a complete answer, I put together a step-by-step guide that I am happy to share with you in the video below.
One of the common patterns of Internet of Things applications is called “cold path” and consists of storing all data produced by IoT devices in the cloud for later processing. To make such an implementation trivial, Azure IoT Hub supports routing of messages coming from devices directly to cloud storage services. IoT Hub can also apply simple rules based on both properties, and the message body can route messages to various custom endpoints of your choice. IoT Hub will write blob content in AVRO format, which has both message body and message properties. Great for data/message preservation, AVRO can be challenging for querying and processing the data. Here is a suggested solution to process this data.
Many of the big data patterns can be used for processing non-relational data files in custom file formats. Focusing on cost and deployment simplicity, Azure Data Lake Analytics (ADLA) is one of the only “pay per query” big data
Microsoft at SAPPHIRE NOW 2018
Enterprises have been embarking on a journey of digital transformation for many years. For many enterprises this journey cannot start or gain momentum until core SAP Enterprise Resource Planning (ERP) landscapes are transformed. The last year has seen an acceleration of this transformation with SAP customers of all sizes like Penti, Malaysia Airlines, Guyana Goldfields , Rio Tinto, Co-op, and Coats migrating to the cloud on Microsoft Azure. This cloud migration, which is central to digital transformation, helps to increase business agility, lower costs, and enable new business processes to fuel growth. In addition, it has allowed them to take advantage of advancements in technology such as big data analytics, self-service business intelligence (BI), and Internet of Things (IOT).
As leaders in enterprise software, SAP and Microsoft provide the preferred foundation for enabling the safe and trusted path to digital transformation. Together we enable the inevitable move to SAP S/4HANA which will help accelerate digital transformation for customers of all sizes.
Microsoft has collaborated with SAP for 20+ years to enable enterprise SAP deployments with Windows Server and SQL Server. In 2016 we partnered to offer SAP certified, purpose-built, SAP HANA on Azure Large Instances
O’Reilly and Microsoft are excited bring you a new e-book on AI, titled A Developer’s Guide to Building AI Applications.
This book, which is clearly developer-focused, walks you through the process of building intelligent cloud-based bots, and makes relevant code samples available from GitHub. As you know, AI is accelerating the digital transformation of every industry on the planet. It is our goal to allow developers and organizations of all stripes to be able to use AI successfully to augment human ingenuity and create the next generation of intelligent apps. Through this new e-book, Anand Raman and Wee Hyong Tok of Microsoft provide a comprehensive roadmap for developers to build their first AI-infused application.
Using the example of a “Conference Buddy”, you’ll learn the key ingredients needed to develop an intelligent chatbot – one that helps the attendees at a conference interact with speakers in a novel way.
The e-book attempts to provide a gentle introduction to the tools, infrastructure and services in the Microsoft AI Platform that allow you to create intelligent applications. More specifically, you will learn about:
How the intersection of cloud, data and AI is enabling developers and organizations all over the world to
https://blogs.msdn.microsoft.com/sql_server_team/clustered-columnstore-index-massively-parallel-trickle-insert/Source: https://blogs.msdn.microsoft.com/sql_server_team/clustered-columnstore-index-massively-parallel-trickle-insert/ A traditional scenario of loading data into CCI is a nightly load from one or more data files containing millions of rows. Recommended technique is to load the data with batchsize >= 102400 as explained https://blogs.msdn.microsoft.com/sqlserverstorageengine/2014/07/27/clustered-column-store-index-bulk-loading-the-data/. However, READ MORE
Microsoft is pleased to announce that the Analytics Platform System (APS) appliance update 7 (AU7) is now generally available. APS is Microsofts scale-out Massively Parallel Processing (MPP) system based on SQL Server for data warehouse specific workloads on-premises.
Customers will get significantly improved query performance and enhanced security features with this release. APS AU7 builds on appliance update 6 (APS 2016) release as a foundation. Upgrading to APS appliance update 6 is a prerequisite to upgrade to appliance update 7.
APS AU7 now provides the ability to automatically create statistics and update of existing outdated statistics for improved query optimization. APS AU7 also adds support for setting multiple variables from a single select statement reducing the number of redundant round trips to the server and improving overall query and ETL performance time. Other T-SQL features include HASH and ORDER GROUP query hints to provide more control over improving query execution plans.
APS AU7 also includes latest firmware and drivers along with the hardware and software patch to address the Spectre/Meltdown vulnerability from our hardware partners.
Customers already on APS2016 will experience an enhanced upgrade process to APS AU7 allowing a shorter maintenance window with