Monat: April 2019

Step up your machine learning process with Azure Machine Learning service

Everyone’s talking about machine learning (ML). Business decision makers are finding ways to deploy machine learning in their organizations. Data scientists are keeping up with all the advancements, tools, and frameworks available. Media outlets are reporting on awe-inspiring breakthroughs in the artificial intelligence revolution.

We believe the way forward lies in democratizing artificial intelligence and machine learning by proxy. This means making machine learning services available to singular data scientists and developers, small to medium sized businesses, and global organizations–all with the ability to scale their models up and out.

This means offering automated and prebuilt algorithms, as well as the ability to create highly customized models. It also means ensuring they are compatible with open source frameworks.

The challenges of machine learning

As you likely already know, machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. But the promises of machine learning come with challenges. Here are just a few:

There is a lot of manual math, data analysis, programming, training, and experimentation.
There are multiple ways to solve every problem.
Challenges arise in monitoring and evaluating the precision, accuracy, and efficacy of a given model.
Data scientists struggle to find the right development tools, debugging tools, and educational resources.

Azure Machine Learning service

The Azure Machine Learning service provides a cloud-based service you can use to develop, train, test, deploy, manage, and track machine learning models. With Automated Machine Learning and other advancements available, training and deploying machine learning models is easier and more approachable than ever.

Below are three of the key pillars of Azure Machine Learning service that give us an edge. I’ll be going into greater detail about each of these pillars in subsequent blogs, so stay tuned!

These three pillars apply largely to automated machine learning, also provided under Azure Machine Learning service. Automated machine learning helps users of all skill levels accelerate their pipelines, leverage open source frameworks, and scale easily. Automated machine learning, a form of deep machine learning, makes machine learning more accessible across an organization.

1. End-to-end ML lifecycle management

There’s a lot that goes into the machine learning lifecycle. Data preparation, experimentation, model training, model management, deployment, and monitoring traditionally require time and manual effort. Azure Machine Learning service seamlessly integrates with Azure services to provide end-to-end capabilities for the entire machine learning lifecycle, making it simpler and faster than ever. With Azure Machine Learning Service, you can:

Create multiple or common workspaces to collaborate easily across teams.
Centralize management of all model artifacts.
Schedule runs in parallel.
Manage scripts and data separately.
Ensure ease of support and maintenance with CI/CD while driving quality over time and preventing model drift.
Easily track your experiments and version your models.
Manage and monitor your models directly in the Azure portal.

2. Power productivity and ease-of-use with an open platform

Data scientists and developers are empowered to easily build and train highly accurate machine learning and even deep-learning models through the frameworks and tools that they’re familiar with. You can now bring machine learning models to market faster with flexible open tools. With Azure Machine Learning, you can:

Use your favorite open source frameworks.
Use a familiar and rich set of tools, such as Jupyter Notebooks, with the Python extension for Visual Studio Code.
Reduce friction and refocus on building models.
Easily leverage the multi-cloud interoperability with built-in ONNX support.

3. Scale up and out to the cloud or edge easily

Previously, machine learning requires powerful compute capabilities in order to train models quickly. With hardware acceleration (GPUs, containers, etc.), scaling up or out is much easier. With Azure Machine Learning, you can:

Use any data and deploy models anywhere.
Scale out training from your local laptop or workstation to the cloud with compute on-demand.
Get GPU and deep learning framework support.
Distribute training for faster results by running models over a cluster of GPU-equipped computers in tandem.
Feel confident in enterprise-grade security, audit, and compliance.
Have reliable model deployment across cloud and edge.
Get cost effective inferencing with batch prediction and scoring.
Consume real-time scoring for targeted outcomes.

As you can see, Azure Machine Learning service provides an effective solution to a number of top concerns for individuals and organizations seeking to deploy machine learning models and are making an effort to advance machine learning for everyone’s benefit. Look out for more upcoming blogs in this series, where we will cover each of these three pillars in more detail.

Learn more

Learn more about the Azure Machine Learning service.

Get started with a free trial of Azure Machine Learning service.
Quelle: Azure

Azure.Source – Volume 76

Hybrid strategy | Preview | Generally available | News & updates | Technical content | Azure shows | Events | Customers, partners, and industries

Build a Successful Hybrid Strategy

Do you have workloads in the cloud & on-premises? Then you know how important it is to have a comprehensive hybrid design and implementation plan. To help you approach hybrid cloud even more effectively, Microsoft announced two new hybrid cloud services: Azure Stack HCI Solutions and Azure Data Box Edge. Whether you need a single or multi-cloud, or are looking to bring intelligent edge computing to your business, you need a consistent and secure environment, no matter where your data resides.

Enabling customers’ hybrid strategy with new Microsoft innovation

The ability for customers to embrace both public cloud and local datacenter, plus edge capability, is enabling customers to improve their IT agility and maximize efficiency. The benefit of a hybrid approach is also what continues to bring customers to Azure, the one cloud that has been uniquely built for hybrid. We haven’t slowed our investment in enabling a hybrid strategy, particularly as this evolves into the new application pattern of using intelligent cloud and intelligent edge. We are continuing to expand Azure Stack offerings to meet a broader set of customer needs, so they can run virtualized applications in their own datacenter. Join the on-demand hybrid cloud virtual event.

Announcing Azure Stack HCI: A new member of the Azure Stack family

Announcing Azure Stack HCI solutions are now available for customers who want to run virtualized applications on modern hyperconverged infrastructure (HCI) to lower costs and improve performance. Azure Stack HCI solutions feature the same software-defined compute, storage, and networking software as Azure Stack, and can integrate with Azure for hybrid capabilities such as cloud-based backup, site recovery, monitoring, and more. Azure Stack HCI solutions are designed to run virtualized applications on-premises in a familiar way, with simplified access to Azure for hybrid cloud scenarios. A great hybrid cloud strategy is one that meets you where you are, delivering cloud benefits to all workloads wherever they reside.

Accelerated AI with Azure Machine Learning service on Azure Data Box Edge

Announcing the preview of Azure Machine Learning hardware accelerated models powered by Project Brainwave on Data Box Edge. This preview enhances Azure Machine Learning service by enabling you to train a TensorFlow model for image classification scenarios, containerize the model in a Docker container, and then deploy the container to a Data Box Edge device with Azure IoT Hub. Applying machine learning models to the data on Data Box Edge provides lower latency and savings on bandwidth costs, while enabling real-time insights and speed to action for critical business decisions.

Azure Data Box family meets customers at the edge

Announcing the general availability of Azure Data Box Edge and the Azure Data Box Gateway. Data Box Edge is an on-premises anchor point for Azure and can be racked alongside your existing enterprise hardware or live in non-traditional environments from factory floors to retail aisles. Data Box Edge comes with a built-in storage gateway. If you don’t need the Data Box Edge hardware or edge compute, then the Data Box Gateway is also available as a standalone virtual appliance that can be deployed anywhere within your infrastructure. You can get these products today in the Azure portal.

Now in preview

New updates to Azure AI expand AI capabilities for developers

Continuing our quest to make Azure the best place to build AI, we have introduced a preview of the new Anomaly Detector Service which uses AI to identify problems so companies can minimize loss and customer impact. We have also announced the general availability of Custom Vision to more accurately identify objects in images. From using speech recognition, translation, and text-to-speech to image and object detection, Azure Cognitive Services makes it easy for developers to add intelligent capabilities to their applications in any scenario.

People Recognition Enhancements – Video Indexer

Announcing Video Indexer enhancements that makes custom Person model training and management faster and easier. Enhancements include a centralized custom Person Model Management page for creating multiple models in your account; giving you the ability to train your account to identify people based on images of people’s faces even before you upload any video. Video Indexer now also supports up to 50 Persons Models per account, where each of the models supports up to 1 million different people. The new Video Indexer features are now in public preview.

Azure Search – New Storage Optimized service tiers available in preview

Announcing the preview of two new service tiers for Storage Optimized workloads in Azure Search. Azure Search is an AI-powered cloud search service for modern mobile and web app development. Azure Search is the only cloud search service with built-in artificial intelligence (AI) capabilities that enrich all types of information to easily identify and explore relevant content at scale. With Azure Search, you spend more time innovating on your websites and applications, and less time maintaining a complex search solution.

Announcing the public preview of Data Discovery & Classification for Azure SQL Data Warehouse

Announcing the public preview of Data Discovery & Classification for Azure SQL Data Warehouse, an additional capability for managing security for sensitive data. Data Discovery & Classification alleviates the pain-point of protecting sensitive data from becoming unmanageable to discover, classify, and protect as your data assets grow. Azure SQL Data Warehouse is a fast, flexible, and secure cloud data warehouse tuned for running complex queries fast and across petabytes of data.

Also available in preview

Public preview: Windows Server container support in Azure App Service
Public preview: Data Discovery & Classification for Azure SQL Data Warehouse
Update 19.03 for Azure Sphere public preview now available in Retail feed

Now generally available

Larger, more powerful Managed Disks for Azure Virtual Machines

Announcing the general availability of larger and more powerful Azure Managed Disk sizes of up to 32 TiB on Premium SSD, Standard SSD, and Standard HDD disk offerings. In addition, we support disk sizes up to 64 TiB on Ultra Disks in preview. We are also increasing the performance scale targets for Premium SSD to 20,000 IOPS and 900 MB/sec. With the general availability (GA) of larger disk sizes, Azure now offers a broad range of disk sizes for your production workload needs, with unmatched scale and performance. Our next step is to enable the preview of Azure Backup for larger disk sizes providing you full coverage for enterprise backup scenarios by the end of May 2019. Similarly, Azure Site Recovery support for on-premises to Azure, and Azure to Azure Disaster Recovery will be extended to all disk sizes soon.

Azure Premium Block Blob Storage is now generally available

Announce general availability of Azure Premium Blob Storage. Premium Blob Storage is a new performance tier in Azure Blob Storage for block blobs and append blobs; complimenting the existing Hot, Cool, and Archive access tiers. Premium Blob Storage is ideal for workloads that require very fast response times and/or high transactions rates, such as IoT, Telemetry, AI, and scenarios with humans in the loop such as interactive video editing, web content, online transactions, and more. Premium Blob Storage is available with Locally-Redundant Storage (LRS) and comes with High-Throughput Block Blobs (HTBB), which provides very high and instantaneous write throughput when ingesting block blobs larger than 256KB. Premium Blob Storage is initially available in US East, US East 2, US Central, US West, US West 2, North Europe, West Europe, Japan East, Australia East, Korea Central, and Southeast Asia regions with more regions to come.

Azure Blob Storage lifecycle management generally available

Announcing the general availability of Blob Storage Lifecycle Management to automate blob tiering and retention with custom defined rules. Azure Blob Storage Lifecycle Management offers a rich, rule-based policy which you can use to transition your data to the best access tier and to expire data at the end of its lifecycle. This feature is available in all Azure public regions.

Azure Storage support for Azure Active Directory based access control generally available

Announcing the general availability of Azure Active Directory (AD) based access control for Azure Storage Blobs and Queues. Enterprises can now grant specific data access permissions to users and service identities from their Azure AD tenant using Azure’s Role-based access control (RBAC).  Administrators can then track individual user and service access to data using Storage Analytics logs. Storage accounts can be configured to be more secure by removing the need for most users to have access to powerful storage account access keys.

Blob storage interface on Data Box is now generally available

Announcing the general availability of a blob storage interface on Data Box. The blob storage interface allows you to copy data into the Data Box via REST and makes the Data Box appear like an Azure storage account. Applications that write to Azure blob storage can be configured to work with the Azure Data Box. With this capability, partners like Veeam, Rubrik, and DefendX are now able to use the Data Box to assist customers moving data to Azure.

Also generally available

Greater storage capacity and performance with new Azure Disks SKU
Azure Security Center change from monthly to hourly unit of measure
Event Hubs resource GUID changes
App Service updating PHP to latest versions
Azure Site Recovery: Firewall support for replication of on-premises machines
Azure API Management roundup of features and fixes

News and updates

Clean up files by built-in delete activity in Azure Data Factory

Azure Data Factory (ADF) is a fully-managed data integration service in Azure that allows you to iteratively build, orchestrate, and monitor your Extract Transform Load (ETL) workflows. You must periodically clean up files from the on-premises or the cloud storage server when the files become out of date. The ADF built-in delete activity, which can be part of your ETL workflow, deletes undesired files without writing code. You can use ADF to delete folder or files from Azure Blob Storage, Azure Data Lake Storage Gen1, Azure Data Lake Storage Gen2, File System, FTP Server, sFTP Server, and Amazon S3.

What’s new in Azure IoT Central – March 2019

This post recaps the new features now available in Azure IoT Central; including embedded Microsoft Flow, updates to the Azure IoT Central connector, Azure Monitor action groups, multiple dashboards, localization support, and highlights the recently expanded Jobs functionality. With these new features, you can more conveniently build workflows as actions and reuse groups of actions, organize your visualizations across multiple dashboards, and work with IoT Central with your favorite language.

Incrementally copy new files by LastModifiedDate with Azure Data Factory

Azure Data Factory (ADF) is the fully-managed data integration service for analytics workloads in Azure. Using ADF, users can load the lake from 80 plus data sources on-premises and in the cloud, use a rich set of transform activities to prep, cleanse, and process the data using Azure analytics engines, while also landing the curated data into a data warehouse for getting innovative analytics and insights. Now, ADF provides a new capability for you to incrementally copy new or changed files only by LastModifiedDate from a file-based store. The feature is available when loading data from Azure Blob Storage, Azure Data Lake Storage Gen1, Azure Data Lake Storage Gen2, Amazon S3, File System, SFTP, and HDFS.

High-Throughput with Azure Blob Storage

Announcing that High-Throughput Block Blob (HTBB) is globally enabled in Azure Blob Storage. HTBB provides significantly improved and instantaneous write-throughput when ingesting larger block blobs, up to the storage account limits for a single blob. We have also removed the guesswork in naming your objects, enabling you to focus on building the most scalable applications. High-Throughput Block Blob is now available in all Azure regions and is automatically active on your existing storage accounts at no extra cost.

Additional news and updates

Happy birthday to managed Open Source RDBMS services in Azure!
Azure Cache for Redis resource GUID changes
IoT Hub supports new Azure Monitor metric alerts
Service Bus Messaging Unit name changes
Video Indexer is now ISO, SOC, HiTRUST, FedRAMP, HIPAA, PCI certified
ExpressRoute Resource GUID name change from "Port" to "Direct"
New tool available to migrate from classic monitoring alerts

Technical content

Building serverless microservices in Azure – sample architecture

Distributed applications take full advantage of living in the cloud to run globally, avoid bottlenecks, and always be available for users worldwide. Most cloud native applications use a microservices architecture to maximize the wide range of managed services for managing infrastructure, scaling, and improving critical processes like deployment or monitoring. This post focuses on how building serverless microservices is a great fit for event-driven scenarios, and how you can use the Azure Serverless platform.

Analysis of network connection data with Azure Monitor for virtual machines

Azure Monitor for virtual machines (VMs) collects network connection data that you can use to analyze the dependencies and network traffic of your VMs. Analyze the number of live and failed connections, bytes sent and received, and the connection dependencies of your VMs down to the process level. Get started with log queries in Azure Monitor for VMs.

Resource governance in Azure SQL Database

When you choose a specific Azure SQL Database service tier, you are selecting a pre-defined set of allocated resources across several dimensions such as CPU, storage type, storage limit, memory, and more. Ideally, you select a service tier that meets the workload demands of your application. With each service tier selection, you are also inherently selecting a set of resource usage boundaries & limits. Learn how to use governance to help set a balanced set of allocated resources.

How to run Ghost blogging software on Azure in a Linux Docker Container

In this post, Jessica details the steps needed for running a Ghost blog in a Docker container on Azure.

Get an official service issue root cause analysis with Azure Service Health

Azure Service Health helps you stay informed and take action when Azure service issues like incidents and planned maintenance affect you by providing a personalized health dashboard, customizable alerts, and expert guidance. Learn to use Azure Service Health’s health history to review past health issues and get official root cause analyses (RCAs) to share with your internal and external stakeholders.

AKS Networking Policies

This blog post looks at securing traffic between pods in Azure Kubernetes Service. It outlines the basics of a demo that demonstrates the process using the Cloud Shell.

How to access Azure Linux virtual machines with Azure Active Directory

In this blog post, Neil Paterson walks through the basic configuration steps for accessing Azure Linux virtual machines using Azure AD credentials.

MSDEV podcast: The MXChip with Suz Hinton

The popular podcast MSDev is joined by Suz Hinton to discuss the MXChip Microcontroller board. Folks learned what it was, why you would use it, and some other technical learnings around hardware and Azure IoT in general.

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Serverless — from the beginning, using Azure Functions (Azure portal), Part I

Part 1 in this series covers the essentials of Serverless computing in the cloud. It defines the term and explains how to get started with Azure Functions in the Azure Portal. This is the first part of five. In this part Chris also looks at Function apps, triggers and bindings, and the practical approaches needed to use Serverless within your apps.

Deploying Deep Learning models using Kubeflow on Azure

In this blog post, we will be looking into two machine learning toolkits Azure Machine Learning service (AML) and Kubeflow to compare the two approaches for a computer vision scenario where one would like to deploy a trained deep learning model for image classification. We hope this will help data scientists make a more informed decision for their next deployment problem.

Azure Stack IaaS – part six

A fundamental quality of a cloud is that it provides an elastic pool for your resource to use when needed. Since you only pay for what you use, you don’t need to over provision. Instead, you can optimize capacity based on demand. See some of the ways you can do this for your IaaS VMs running in Azure and Azure Stack. Azure and Azure Stack makes it easy for you to resize, scale out, add and remove your VM from the portal.

Additional technical content

Azure Developer – Get the list of conference rooms using Microsoft Graph API programmatically
Preparing for AZ-300 and AZ-301 with Pluralsight courses

Azure shows

Episode 272 – The New Azure Monitor | The Azure Podcast

Shankar Sivadasan, a Senior Azure Product Marketing Manager, gives us all the details on how the trusty Azure Monitor service has evolved into the main monitoring solution in Azure.

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Deploy to Azure using GitHub Actions | Azure Friday

Gopi joins Donavan to discuss how to deploy to Azure using GitHub Actions, which helps you to configure CI/CD from the GitHub UI.

Using GitHub Actions to Deploy to Azure | The DevOps Lab

Damian sits down with Product Manager Gopinath Chigakkagari to talk about deploying to Azure using GitHub Actions. In this episode, Gopi walks through a deployment process inside GitHub Actions to deploy a containerized application to Azure on a new push to a repository. Along the way, he'll also show some of the features and advantages of GitHub Actions itself.

Azure IoT Certification Service | Internet of Things Show

Azure IoT Certification Service can streamline your IoT device certification processes and reduce validation processes for device manufacturers.

Five Ways You Can Build Mobile Apps with JavaScript | Five Things

Why are there so many options for developing mobile apps? What should you choose? How can you slipstream your way into mobile and take advantage of the cloud? Todd Anglin has all the answers and wears some snazzy clothing, in this episode of Five Things.

Investigating Production Issues with Azure Monitor and Snapshot Debugger | On .NET

In this episode, Isaac Levin joins us to share how the developer exception resolution experience can be better with Azure Monitor and Snapshot Debugger. The discussion talks about what Azure Monitor is and an introduction to Snapshot Debugger, and quickly goes into demos showcasing what developers can do with Snapshot Debugger.

Using Ethereum Logic Apps to push ledger data into to a MySQL or PostgreSQL database | Block Talk

In this episode we show how to use the Ethereum Logic App connector to integrate a ledger with common backend systems like popular open-source databases, MySQL and PostgreSQL.

How to add Azure Alerts as push notifications on your phone | Azure Portal Series

The Azure mobile app allows you to receive Azure Alerts as push notifications on your mobile device. In this video of the Azure Portal “How To” Series, learn how you can setup Azure Alerts such as metric alerts, log analytics, Application Insights, and Activity Log from Azure Monitor on the Azure portal.

How to use Azure Automation with PowerShell | Azure Tips and Tricks

In this edition of Azure Tips and Tricks, learn how to use Azure Automation with a Windows Machine with PowerShell. Azure Automation makes it easy to do common tasks like, scaling Azure SQL Database up and down and starting and stopping a virtual machine.

Matt Mitrik on GitHub with Azure Boards | Azure DevOps Podcast

Jeffrey Palermo and Matt Mitrik discuss GitHub with Azure Boards. They talk about the level of integration that’s going to be in Azure Boards (how they’re thinking about things right now and where they want to go), their efforts towards new project workflow and integration for Azure Boards, and the timeline Matt’s team is looking at for these changes. Matt also gives his pitch for GitHub as the future premiere offering and why you should consider migrating.

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Episode 4 – Azure Enthusiast: Kevin Boland | AzureABILITY

AzureABILITY host Louis Berman talks Azure with Bentley Systems' Kevin Boland—an Enterprise Cloud Architect who manages one of the largest and most complex set of Azure deployments on the planet.

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Additional Azure shows & videos

Azure Container Registry (ACR) repository and tag locking | Azure Friday
What is Azure Mixed Reality Services? | One Dev Question | One Dev Minute 
What are you most proud of on HoloLens 2? | One Dev Question | One Dev Minute
What are logic apps? | One Dev Question
What can I use Azure Function Triggers for? | One Dev Question
Microsoft Azure Security Center for IoT | Azure Security
Secure your IoT solution with Microsoft Azure | Azure Security
How to set up your first Azure Service Health alert | Maintenance and Resilience in Azure
What's single sign on for SaaS applications? | Azure Active Directory
How to deploy single sign on for SaaS applications | Azure Active Directory
How to roll out single sign on for SaaS applications | Azure Active Directory

Events

Hannover Messe 2019: Azure IoT Platform updates power new, highly-secured Industrial IoT Scenarios

Hannover Messe 2019 is taking place this week (01-05 April) in Hannover, Germany and Azure is there. Manufacturing continues to be one of the leading industries adopting IoT for a growing set of scenarios to improve safety, efficiency, and reliability for people and devices. We’ve made several significant additions to our IoT platform to address these needs; including the launch of Azure Digital Twins and Azure Sphere, and the general availability of Azure IoT Central and Azure IoT Edge. Introducing a set of new product capabilities and programs that make it easier for our customers to build enterprise-grade industrial IoT solutions with open standards, while ensuring security and innovation protection across cloud boundaries.

Customers, partners, and industries

Azure Sphere ecosystem accelerates innovation

How can device builders bring a high level of security to the billions of network-connected devices expected to be deployed in the next decade? It starts with building security into your IoT solution from the silicon up. In this post, you learn about the holistic device security of Azure Sphere and how the expansion of the Azure Sphere ecosystem is helping to accelerate the process of taking secure solutions to market.

Why IoT is not a technology solution—it's a business play

To help you plan your IoT journey, we’re rolling out a four-part blog series. In the upcoming posts, we’ll cover how to create an IoT business case, overcome capability gaps, and simplify execution; all advice to help you maximize your gains with IoT. In this first post, explore the mindset it takes to build IoT into your business model.

Umanis lifts the hood on their AI implementation methodology

Umanis, a systems integrator and preferred AI training partner based in France, has been innovating in Big Data and Analytics in numerous verticals for more than 25 years and has developed an effective methodology for guiding customers into the Intelligent Cloud. Umanis has found it to be a robust way of rolling out end-to-end data and AI projects while minimizing friction and risk. By using this approach to present a Data & AI project to both customers and internal teams, everyone can get a good feeling of what activities, technologies, and challenges are involved.

Azure Marketplace new offers – Volume 34

The Azure Marketplace is the premier destination for all your software needs – certified and optimized to run on Azure. Find, try, purchase, and provision applications & services from hundreds of leading software providers. You can also connect with Gold and Silver Microsoft Cloud Competency partners to help your adoption of Azure. In the second half of February we published 50 new offers.

Azure Windows Virtual Desktop in public preview and a big win for Cosmos DB | A Cloud Guru – Azure This Week

This time on Azure This Week, Lars covers Windows Virtual Desktop in public preview, Azure Cosmos DB gets another big win, and Microsoft and NVIDIA extend video analytics to the intelligent edge.

Quelle: Azure