Kategorie: Microsoft Azure

Azure.Source – Volume 62

KubeCon North America 2018

KubeCon North America 2018: Serverless Kubernetes and community led innovation!

Brendan Burns, Distinguished Engineer in Microsoft Azure and co-founder of the Kubernetes project, provides a welcome to KubeCon North America 2018, which took place last week in Seattle. In his post, Brendan provides a retrospective on Azure Kubernetes Services (AKS), including how engineers at companies such as Maersk, Siemens, and Bosch benefited from adoption of AKS in their solutions. He also provides an overview of the various announcements we made at KubeCon. With Docker, Bitnami, Hashicorp, and others we announced the Cloud Native Application Bundle (CNAB) specification, which is a new distributed application package that combines Helm or other configuration tools with Docker images to provide a complete, self-installing cloud applications. He also announced that Microsoft is donating the likeness of Phippy, and all of your favorites from the Children’s Illustrated Guide to Kubernetes to the CNCF, and the release of a special second episode of the guide, Phippy Goes to the Zoo, which covers ingresses, CronJobs, CRDs, and more.

A hybrid approach to Kubernetes

Azure Stack enables you to run your containers on-premise in pretty much the same you as you do with global Azure. Microsoft Azure Stack is a hybrid cloud platform that lets you deliver services from your datacenter. As a service provider, you can offer services to your tenants. The Kubernetes Cluster Marketplace item 0.3.0 for Azure Stack is consistent with Azure since the template is generated by the Azure Container Service Engine, the resulting cluster will run the same containers as in AKS. It also complies with the Cloud Native Foundation. The cluster depends on an Ubuntu server, custom script, and the Kubernetes items to be in the Azure Stack Marketplace.

Now in preview

Microsoft previews neural network text-to-speech

Speech Service, part of Azure Cognitive Services now offers a neural network-powered text-to-speech capability. Neural Text-to-Speech makes the voices of your apps nearly indistinguishable from the voices of people. Use it to make conversations with chatbots and virtual assistants more natural and engaging, to convert digital texts such as e-books into audiobooks and to upgrade in-car navigation systems with natural voice experiences and more. This release includes significant enhancements since we first revealed Neural Text-to-Speech at Ignite earlier this year, such as: enhanced voice quality, accelerated runtime performance, and greater service availability. With these updates, Speech Services Neural Text-to-Speech capability offers the most natural-sounding voice experience for your users in comparison to the traditional and hybrid system approaches.

Native Python support on Azure App Service on Linux: new public preview!

Built-in Python images for Azure App Service on Linux are now available in public preview. With the choice of Python 3.7, 3.6 and soon 2.7, developers can get started quickly and deploy Python applications to the cloud, including Django and Flask, and leverage the full suite of features of Azure App Service on Linux. When you use the official images for Python on App Service on Linux, the platform automatically installs the dependencies specified in the requirements.txt​ file. While the underlying infrastructure of Azure App Service on Linux has been generally available (GA) for over a year, at the moment we’re releasing the runtime for Python in public preview, with GA expected in a few months.

Automatic performance monitoring in Azure SQL Data Warehouse (preview)

The preview of Query Store for Azure SQL Data Warehouse is now available in preview for both our Gen1 and Gen2 offers. The Query Store contains three actual stores: a plan store for persisting the execution plan information, a runtime stats store for persisting the execution statistics information, and a wait stats store for persisting wait stats information. Query Store is a set of internal stores and Dynamic Management Views (DMVs). These stores are managed automatically by SQL Data Warehouse and provide an unlimited number of queries storied over the last 7 days at no additional charge. Query Store is available in all Azure regions with no additional charge.

Also in preview

Connect Cognitive Services subscription to enable unlimited skillset execution
Python images for App Service Linux are now in preview
MongoDB to Azure Cosmos DB migration is in preview

Now generally available

Azure Monitor for containers now generally available

Azure Monitor for containers monitors the health and performance of Kubernetes clusters hosted on Azure Kubernetes Service (AKS). Since the public preview, we have added several capabilities including: Multi-cluster view, Performance Grid view, Live debugging, and automated onboarding Azure Monitor for containers. Azure Monitor for containers gives you performance visibility by collecting memory and processor metrics from controllers, nodes, and containers that are available in Kubernetes through the Metrics API. After you enable monitoring from Kubernetes clusters, metrics and logs are automatically collected for you through a containerized version of the Log Analytics agent for Linux and stored in your Log Analytics workspace.

Streamlined IoT device certification with Azure IoT certification service

Azure IoT certification service (AICS), a new web-based test automation workflow, is now generally available. AICS will significantly reduce the operational processes and engineering costs for hardware manufacturers to get their devices certified for Azure Certified for IoT program and be showcased on the Azure IoT device catalog. The goals of the certification program are to showcase the right set of IoT devices for industry-specific vertical solutions and to simplify IoT device development. AICS helps achieve these goals by delivering a consistent certification process through automation, additional tests to support validation of device twins and direct methods with IoT Hub primitives, flexibility for customized test cases, and a simple and intuitive user experience.

Static websites on Azure Storage now generally available

Static websites are websites that can be loaded and served statically from a pre-defined set of files. You can now build a static website using HTML, CSS, and JavaScript files that are hosted on Azure Storage. Static websites can be powerful with the use of client-side JavaScript. Azure Storage makes hosting of websites easy and cost-efficient. You can enable static website hosting using the Azure portal, Azure CLI, or Azure PowerShell, which creates a container named ‘$web’. You can then upload your static content to this container for hosting. Your content will be available through a web endpoint. There are no additional charges for enabling static websites on Azure Storage.

Also generally available

Azure Monitor for containers is now available
General availability: Azure Kubernetes Service in East Asia

News and updates

Azure HDInsight integration with Data Lake Storage Gen2 preview – ACL and security update

This integration will enable HDInsight customers to drive analytics from the data stored in Azure Data Lake Storage Gen 2 using popular open source frameworks such as Apache Spark, Hive, MapReduce, Kafka, Storm, and HBase in a secure manner. Azure Data Lake Storage Gen2 unifies the core capabilities from the first generation of Azure Data Lake with a Hadoop compatible file system endpoint now directly integrated into Azure Blob Storage. HDInsight and Azure Data Lake Storage Gen2 integration is based upon user-assigned managed identity. You assign appropriate access to HDInsight with your Azure Data Lake Storage Gen2 accounts. Once configured, your HDInsight cluster is able to use Azure Data Lake Storage Gen2 as its storage.

Azure Backup Server now supports SQL 2017 with new enhancements

You can now install Azure Backup Server on Windows Server 2019 with SQL 2017 as its database. With Azure Backup Server, you can protect application workloads such as Hyper-V VMs, Microsoft SQL Server, SharePoint Server, Microsoft Exchange, and Windows clients from a single console. Azure Backup Server version 3 (MABS V3) is the latest upgrade, and includes critical bug fixes, Windows Server 2019 support, SQL 2017 support and other features and enhancements. MABS V3 is a full release, and can be installed directly on Windows Server 2016, Windows Server 2019, or can be upgraded from MABS V2. Before you upgrade to or install Backup Server V3, read the installation prerequisites.

Azure Functions now supported as a step in Azure Data Factory pipelines

Azure Functions is a serverless compute service that enables you to run code on-demand without having to explicitly provision or manage infrastructure. Using Azure Functions, you can run a script or piece of code in response to a variety of events. Azure Data Factory (ADF) is a managed data integration service in Azure that allows you to iteratively build, orchestrate, and monitor your Extract Transform Load (ETL) workflows. Azure Functions is now integrated with ADF, enabling you to run an Azure function as a step in your data factory pipelines. To run an Azure Function, you need to create a linked service connection and an activity that specifies the Azure Function that you plan to execute.

Automate Always On availability group deployments with SQL Virtual Machine resource provider

High availability architectures are designed to continue to function even when there are database, hardware, or network failures. Azure Virtual Machine instances using Premium Storage for all operating system disks and data disks offers 99.9 percent availability. This SLA is impacted by three scenarios – unplanned hardware maintenance, unexpected downtime, and planned maintenance. You now have a new, automated method to configure Always On availability groups (AG) for SQL Server on Azure VMs with SQL VM resource provider (RP) as a simple and reliable alternative to manual configuration. SQL VM resource provider automates Always On AG setup by orchestrating the provisioning of various Azure resources and connecting them to work together.

Additional news and updates

Azure Database for MariaDB name changes
Azure databases for MySQL and PostgreSQL resource GUID changes

Technical content

Power BI and Azure Data Services dismantle data silos and unlock insights

Power BI data flows, the Common Data Model, and Azure Data Services can be used together to break open silos of data in your organization and enable business analysts, data engineers, and data scientists to share data to fuel advanced analytics and unlock new insights to give you a competitive edge. Learn how to connect Power BI and Azure Data Services to share data and unlock new insights with a new tutorial. The tutorial gives you a first look at how to use CDM folders to share data between Power BI and Azure Data Services. The tutorial uses sample libraries, code, and Azure resource templates that you can use with CDM folders that you create from your own data. By working through the tutorial, you’ll see first-hand how the metadata stored in a CDM folder makes it easier to for each service to understand and share data.

Deploying Apache Airflow in Azure to build and run data pipelines

Apache Airflow is an open source platform used to author, schedule, and monitor workflows. Airflow overcomes some of the limitations of the cron utility by providing an extensible framework that includes operators, programmable interface to author jobs, scalable distributed architecture, and rich tracking and monitoring capabilities. We developed an Azure Quickstart template that enables you to deploy and create an Airflow instance in Azure more quickly by using Azure App Service and an instance of Azure Database for PostgreSQL as a metadata store.

How news platforms can improve uptake with Microsoft Azure’s Video AI service

Microsoft News is an app that delivers breaking news and trusted, in-depth reporting from the world's best journalists. Microsoft News created advanced algorithms to analyze their articles and determine how to increase personalization, which ultimately increases consumption, but wanted more insight on their videos. Anna Thomas, an Applied Data Scientist within Microsoft Engineering, set off to determine how to deliver these insights using a combination of Microsoft technologies and custom solutions; however, she discovered that the Video Indexer API held more capabilities than she expected. Check out her post to see what she discovered.

Know exactly how much it will cost for enabling DR to your Azure VMs

Azure offers built-in disaster recovery (DR) solution for Azure Virtual Machines through Azure Site Recovery (ASR). Site Recovery manages and orchestrates disaster recovery of on-premises machines and Azure virtual machines (VMs), including replication, failover, and recovery. A common question we get is about costs associated with configuring DR for Azure virtual machines, so Sujay Talasila explored how to estimate DR costs. Follow his example to explore how much it will cost to support your particular solution. Disaster Recovery between Azure regions is available in all Azure regions where ASR is available.

Taking a closer look at Python support for Azure Functions

As announced at Microsoft Connect(); 2018 earlier this month, you can now develop your Functions using Python 3.6, based on the open-source Functions 2.0 runtime and publish them to a Consumption plan (pay-per-execution model) in Azure. Python is a great fit for data manipulation, machine learning, scripting, and automation scenarios. Building these solutions using serverless Azure Functions can take away the burden of managing the underlying infrastructure, so you can move fast and actually focus on the differentiating business logic of your applications. Read this post for details about the newly announced features and dev experiences for Python Functions.

Additional technical content

Kubernetes Pod Security 101
Flipping the static site switch for Azure Blob Storage programmatically
How to Launch a Dockerized Node.js App Using the Azure Web App for Containers Service

Azure shows

Episode 258 – Live from KubeCon 2018 | The Azure Podcast

We are live at KubeCon+CloudNative in Seattle where Microsoft, together with the who's-who of the tech world, are talking about Kubernetes, We are very fortunate to get Lachie Evenson, Principal PM in the Azure team, Tommy Falgout, a Cloud Solution Architect and Daniel Selman, a Kubernetes Consultant, together in a room to discuss the current state of Kubernetes and AKS.

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How to get started with Docker and Azure | Azure Tips and Tricks

Learn how you can get started using Docker and Azure. To get started with Docker, make sure you have the Docker desktop application installed on your local dev machine.

How to deploy an image classification model using Azure services

Learn how to deploy an image classification model using Azure Machine Learning service. In this tutorial, you'll use Azure Machine Learning service to set up your testing environment, retrieve the model from your work space, and test the model locally. You’ll then see how to deploy the model to Azure Container Instance (ACI) and test the deployed model to Azure Kubernetes Service (AKS).

Decentralized Identity and Blockchain | Block Talk

This video introduces the concept of decentralized identity and how blockchain enables hosting these identities in a decentralized fashion. The demo provides a walkthrough of a decentralized identity that is anchored on Ethereum blockchain and is consumed using uPort application.

Running AI on IoT microcontroller devices with ELL | The IoT Show

How about designing and deploying intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit? How interesting would that be? This is exactly what the open source Embedded Learning Library (ELL) project is about. The deployed models run locally, without requiring a network connection and without relying on servers in the cloud. ELL is an early preview of the embedded AI and machine learning technologies developed at Microsoft Research. Chris Lovett from Microsoft Research gives us a fantastic demo of the project in this episode of the IoT Show.

AzureIoT TypeEdge : a strongly-typed development experience for Azure IoT Edge | The IoT Show

Are you excited about Azure IoT Edge? Then you are going to love TypeEdge because it simplifies the IoT Edge development down to a simple F5 experience. Watch how you can now create a complete Azure IoT Edge application from scratch in your favorite development environment, in just a few minutes.

LearnAI: Adding Bing Search to Bots | AI Show

The LearnAI team has updated the Azure Cognitive Services Bootcamp! Tune in to get an overview of the changes and a walk through of how you can add Bing Search, LUIS, and Azure Search to bots via the Bot Framework SDK V4.

LearnAI: LUIS – Notes from the Field | AI Show

Anna Thomas has been collecting notes for the past two years from field members (internal and external) who have developed complex LUIS models. In this video, we'll explore some of the limitations or challenges that are faced when you try to deploy enterprise-ready LUIS models at scale, and how they can be addressed.

Jeremy Epling on Azure Pipelines – Episode 014 | The Azure DevOps Podcast

Jeffrey Palermo is joined by Jeremy Epling, Head of Product for Azure Pipelines and a Principal Group Program Manager at Microsoft. He has been a leader at Microsoft for over 15 years in various roles. There’s a lot going on in the DevOps space with Azure right now — and in particular, with Azure Pipelines. Jeremy is incredibly passionate about the current progress being made and is excited to discuss all the new features coming to Pipelines in today’s episode!

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Customers, partners, and industries

Cloud Commercial Communities webinar and podcast update

Check out the Cloud Commercial Communities monthly webinar and podcast update, which provides a comprehensive list of forthcoming (three scheduled for today) and on-demand content. Each month the Industry Experiences team focuses on core programs, updates, trends, and technologies that Microsoft partners and customers need to know to increase success using Azure and Dynamics.

 

An Azure Function orchestrates a real-time, serverless, big data pipeline

Although it’s not a typical use case for Azure Functions, a single Azure function is all it took to fully implement an end-to-end, real-time, mission-critical data pipeline for a fraud detection scenario. The solution was built on an architectural pattern common for big data analytic pipelines, with massive volumes of real-time data ingested into a cloud service where a series of data transformation activities provided input for a machine learning model to deliver predictions. Kate Baroni, Software Architect at Microsoft Azure, provides an overview of the solution, which is covered in the Mobile Bank Fraud Solution Guide with details on the architecture and implementation.

Extracting insights from IoT data using the warm path data flow

If you are responsible for the machines on a factory floor, you are already aware that the Internet of Things (IoT) is the next step in improving your processes and results. Having sensors on machines, or the factory floor, is the first step. The next step is to use the data. In this post, Ercenk Keresteci Principal Solutions Architect, Industry Experiences, highlights another scenario from the Extracting Insights from IoT solution guide, which provides a technical overview of the components needed to extract actionable insights from IoT data analytics. This post covers the speed layer (warm path), which analyze data in real time. This layer is designed for low latency, at the expense of accuracy. It is a faster-processing pipeline that archives and displays incoming messages, and analyzes these records, generating short-term critical information and actions such as alarms.

Extracting insights from IoT data using the cold path data flow

In a further exploration of the guide described above, this post covers the batch and serving layers (cold path), which stores all incoming data in its raw form and performs batch processing on the data. The result of this processing is stored as a batch view. It is a slow-processing pipeline, executing complex analysis, combining data from multiple sources over a longer period (such as hours or days), and generating new information such as reports and machine learning models.

How smart buildings can help combat climate change

Fast-paced urbanization offers an exciting opportunity to immediately reduce climate impacts. Because buildings—office complexes, multifamily housing, hotels, stores, schools, hospitals, and malls, among others—comprise a big part of city infrastructure, making them smarter can dramatically lower the energy and carbon footprint of a city. Read this post to learn how connected building technology can manage lighting, heating, and cooling, reducing unnecessary use while maximizing usability and comfort. In addition, you will learn how smart building software can schedule preventive maintenance, automatically identify and prioritize issues for resolution by cost and impact, and continually optimize buildings for comfort and energy efficiency.

Creating a smart grid with technology and people

Utilities and their partners are searching for new solutions that can meet 21st-century energy challenges: surging demand for electricity, two-way energy flow, increased use of clean energy sources, and stairstep approaches to creating a smart grid to tackle the thorniest challenges first. This post provides a look at the digital transformation of the power and utilities industry that is picking up steam. In the very near future, power generation companies will have greater options in how they run their businesses, using IoT-enabled insights to strategically stairstep their way to creating a smart grid and ensure business continuity.

Azure Marketplace new offers – Volume 26

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. During September and October, 149 new consulting offers successfully met the onboarding criteria and went live.

Azure Marketplace new offers – Volume 27

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. From November 1 to November 16, 2018, 61 new offers successfully met the onboarding criteria and went live.

A Cloud Guru's Azure This Week – 14 December 2018

This time on Azure This Week, Lars talks about Azure Machine Learning service now in general availability, Business Critical service tier in Azure SQL Database Managed Instance in general availability, Azure Cosmos DB .NET SDK V3.0 in public preview and a new Azure API Management tier for serverless architectures.

Quelle: Azure

Fine-tune natural language processing models using Azure Machine Learning service

In the natural language processing (NLP) domain, pre-trained language representations have traditionally been a key topic for a few important use cases, such as named entity recognition (Sang and Meulder, 2003), question answering (Rajpurkar et al., 2016), and syntactic parsing (McClosky et al., 2010).

The intuition for utilizing a pre-trained model is simple: A deep neural network that is trained on large corpus, say all the Wikipedia data, should have enough knowledge about the underlying relationships between different words and sentences. It should also be easily adapted to a different domain, such as medical or financial domain, with better performance than training from scratch.

Recently, a paper called “BERT: Bidirectional Encoder Representations from Transformers” was published by Devlin, et al, which achieves new state-of-the-art results on 11 NLP tasks, using the pre-trained approach mentioned above. In this technical blog post, we want to show how customers can efficiently and easily fine-tune BERT for their custom applications using Azure Machine Learning Services. We open sourced the code on GitHub.

Intuition behind BERT

The intuition behind the new language model, BERT, is simple yet powerful. Researchers believe that a large enough deep neural network model, with large enough training corpus, could capture the relationship behind the corpus. In NLP domain, it is hard to get a large annotated corpus, so researchers used a novel technique to get a lot of training data. Instead of having human beings label the corpus and feed it into neural networks, researchers use the large Internet available corpus – BookCorpus (Zhu, Kiros et al) and English Wikipedia (800M and 2,500M words respectively). Two approaches, each for different language tasks, are used to generate the labels for the language model.

Masked language model: To understand the relationship between words. The key idea is to mask some of the words in the sentence (around 15 percent) and use those masked words as labels to force the models to learn the relationship between words. For example, the original sentence would be:

The man went to the store. He bought a gallon of milk.

And the input/label pair to the language model is:

Input: The man went to the [MASK1]. He bought a [MASK2] of milk.
Labels: [MASK1] = store; [MASK2] = gallon

Sentence prediction task: To understand the relationships between sentences. This task asks the model to predict whether sentence B, is likely to be the next sentence following a given sentence A. Using the same example above, we can generate training data like:

Sentence A: The man went to the store.
Sentence B: He bought a gallon of milk.
Label: IsNextSentence

Applying BERT to customized dataset

After BERT is trained on a large corpus (say all the available English Wikipedia) using the above steps, the assumption is that because the dataset is huge, the model can inherit a lot of knowledge about the English language. The next step is to fine-tune the model on different tasks, hoping the model can adapt to a new domain more quickly. The key idea is to use the large BERT model trained above and add different input/output layers for different types of tasks. For example, you might want to do sentiment analysis for a customer support department. This is a classification problem, so you might need to add an output classification layer (as shown on the left in the figure below) and structure your input. For a different task, say question answering, you might need to use a different input/output layer, where the input is the question and the corresponding paragraph, while the output is the start/end answer span for the question (see the figure on the right). In each case, the way BERT is designed can enable data scientists to plug in different layers easily so BERT can be adapted to different tasks.

Figure 1. Adapting BERT for different tasks (Source)

The image below shows the result for one of the most popular dataset in NLP field, the Stanford Question Answering Dataset (SQuAD).

Figure 2. Reported BERT performance on SQuAD 1.1 dataset (Source).

Depending on the specific task types, you might need to add very different input/output layer combinations. In the GitHub repository, we demonstrated two tasks, General Language Understanding Evaluation (GLUE) (Wang et al., 2018) and Stanford Question Answering Dataset (SQuAD) (Rajpurkar and Jia et al., 2018).

Using the Azure Machine Learning Service

We are going to demonstrate different experiments on different datasets. In addition to tuning different hyperparameters for various use cases, Azure Machine Learning service can be used to manage the entire lifecycle of the experiments. Azure Machine Learning service provides an end-to-end cloud-based machine learning environment, so customers can develop, train, test, deploy, manage, and track machine learning models, as shown below. It also has full support for open-source technologies, such as PyTorch and TensorFlow which we will be using later.

Figure 3. Azure Machine Learning Service Overview

What is in the notebook

Defining the right model for specific task

To fine-tune the BERT model, the first step is to define the right input and output layer. In the GLUE example, it is defined as a classification task, and the code snippet shows how to create a language classification model using BERT pre-trained models:

model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)

logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
probabilities = tf.nn.softmax(logits, axis=-1)
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)

Set up training environment using Azure Machine Learning service

Depending on the size of the dataset, training the model on the actual dataset might be time-consuming. Azure Machine Learning Compute provides access to GPUs either for a single node or multiple nodes to accelerate the training process. Creating a cluster with one or multiple nodes on Azure Machine Learning Compute is very intuitive, as below:

compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC24s_v3',
min_nodes=0,
max_nodes=8)
# create the cluster
gpu_compute_target = ComputeTarget.create(ws, gpu_cluster_name, compute_config)
gpu_compute_target.wait_for_completion(show_output=True)
estimator = PyTorch(source_directory=project_folder,
compute_target=gpu_compute_target,
script_params = {…},
entry_script='run_squad.azureml.py',
conda_packages=['tensorflow', 'boto3', 'tqdm'],
node_count=node_count,
process_count_per_node=process_count_per_node,
distributed_backend='mpi',
use_gpu=True)

Azure Machine Learning is greatly simplifying the work involved in setting up and running a distributed training job. As you can see, scaling the job to multiple workers is done by just changing the number of nodes in the configuration and providing a distributed backend. For distributed backends, Azure Machine Learning supports popular frameworks such as TensorFlow Parameter server as well as MPI with Horovod, and it ties in with the Azure hardware such as InfiniBand to connect the different worker nodes to achieve optimal performance. We will have a follow up blogpost on how to use the distributed training capability on Azure Machine Learning service to fine-tune NLP models.

For more information on how to create and set up compute targets for model training, please visit our documentation.

Hyper Parameter Tuning

For a given customer’s specific use case, model performance depends heavily on the hyperparameter values selected. Hyperparameters can have a big search space, and exploring each option can be very expensive. Azure Machine Learning Services provide an automated machine learning service, which provides hyperparameter tuning capabilities and can search across various hyperparameter configurations to find a configuration that results in the best performance.

In the provided example, random sampling is used, in which case hyperparameter values are randomly selected from the defined search space. In the example below, we explored the learning rate space from 1e-4 to 1e-6 in log uniform manner, so the learning rate might be 2 values around 1e-4, 2 values around 1e-5, and 2 values around 1e-6.

Customers can also select which metric to optimize. Validation loss, accuracy score, and F1 score are some popular metrics that could be selected for optimization.

from azureml.train.hyperdrive import *
import math

param_sampling = RandomParameterSampling( {
'learning_rate': loguniform(math.log(1e-4), math.log(1e-6)),
})

hyperdrive_run_config = HyperDriveRunConfig(
estimator=estimator,
hyperparameter_sampling=param_sampling,
primary_metric_name='f1',
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,
max_total_runs=16,
max_concurrent_runs=4)

For each experiment, customers can watch the progress for different hyperparameter combinations. For example, the picture below shows the mean loss over time using different hyperparameter combinations. Some of the experiments can be terminated early if the training loss doesn’t meet expectations (like the top red curve).

Figure 4. Mean loss for training data for different runs, as well as early termination

For more information on how to use the Azure ML’s automated hyperparameter tuning feature, please visit our documentation on tuning hyperparameters. And for how to track all the experiments, please visit the documentation on how to track experiments and metrics.

Visualizing the result

Using the Azure Machine Learning service, customers can achieve 85 percent evaluation accuracy when fine-tuning MRPC in GLUE dataset (it requires 3 epochs for BERT base model), which is close to the state-of-the-art result. Using multiple GPUs can shorten the training time and using more powerful GPUs (say V100) can also improve the training time. For one of the specific experiments, the details are as below:

 

GPU#
1
2
4

K80 (NC Family)
191 s/epoch
105 s/epoch
60 s/epoch

V100 (NCv3 Family)
36 s/epoch
22 s/epoch
13 s/epoch

Table 1. Training time per epoch for MRPC in GLUE dataset

For SQuAD 1.1, customers can achieve around 88.3 F1 score and 81.2 Exact Match (EM) score. It requires 2 epochs using BERT base model, and the time for each epoch is shown below:

 

GPU#
1
2
4

K80 (NC Family)
16,020 s/epoch
8,820 s/epoch
4,020 s/epoch

V100 (NCv3 Family)
2,940 s/epoch
1,393 s/epoch
735 s/epoch

Table 2. Training time per epoch for SQuAD dataset

After all the experiments are done, the Azure Machine Learning service SDK also provides a summary visualization on the selected metrics and the corresponding hyperparameter(s). Below is an example on how learning rate affects validation loss. Throughout the experiments, the learning rate has been changed from around 7e-6 (the far left) to around 1e-3 (the far right), and the best learning rate with lowest validation loss is around 3.1e-4. This chart can also be leveraged to evaluate other metrics that customers want to optimize.

Figure 5. Learning rate versus validation loss

Summary

In this blog post, we showed how customers can fine-tune BERT easily using the Azure Machine Learning service, as well as topics such as using distributed settings and tuning hyperparameters for the corresponding dataset. We also showed some preliminary results to demonstrate how to use Azure Machine Learning service to fine tune the NLP models. All the code is available on the GitHub repository. Please let us know if there are any questions or comments by raising an issue in the GitHub repo.

References

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding and its GitHub site.

Visit the Azure Machine Learning service homepage today to get started with your free-trial.
Learn more about Azure Machine Learning service.

Quelle: Azure

Azure Functions now supported as a step in Azure Data Factory pipelines

Azure Functions is a serverless compute service that enables you to run code on-demand without having to explicitly provision or manage infrastructure. Using Azure Functions, you can run a script or piece of code in response to a variety of events. Azure Data Factory (ADF) is a managed data integration service in Azure that allows you to iteratively build, orchestrate, and monitor your Extract Transform Load (ETL) workflows. Azure Functions is now integrated with ADF, allowing you to run an Azure function as a step in your data factory pipelines.

Simply drag an “Azure Function activity” to the General section of your activity toolbox to get started.

You need to set up an Azure Function linked service in ADF to create a connection to your Azure Function app.

Provide the Azure Function name, method, headers, and body in the Azure Function activity inside your data factory pipeline.

You can also parameterize your function name using rich expression support in ADF. Get more information and detailed steps on using Azure Functions in Azure Data Factory pipelines.

Our goal is to continue adding features and improve the usability of Data Factory tools. Get started building pipelines easily and quickly using Azure Data Factory. If you have any feature requests or want to provide feedback, please visit the Azure Data Factory forum.
Quelle: Azure

Streamlined IoT device certification with Azure IoT certification service

For over three years, we have helped customers find devices that work with Azure IoT technology through the Azure Certified for IoT program and the Azure IoT device catalog. In that time, our ecosystem has grown to one of the largest in the industry with more than 1,000 devices and starter kits from over 250 partners.

Today, we are taking steps to further to grow our device partner ecosystem with the release of Azure IoT certification service (AICS), a new web-based test automation workflow, which is now generally available. AICS will significantly reduce the operational processes and engineering costs for hardware manufacturers to get their devices certified for Azure Certified for IoT program and be showcased on the Azure IoT device catalog.

Over the past year, we’ve made significant improvements to the program such as improving the discovery of certified devices in the Azure Certified for IoT device catalog and expanding the program to support Azure IoT Edge devices. The goal of our certification program is simple – to showcase the right set of IoT devices for our customers’ industry specific vertical solutions and simplify IoT device development.

AICS is designed and engineered to help achieve these goals, delivering on four key areas listed below:

Consistency

AICS is a web-based test automation workflow that can work on any operating systems and web browser. AICS communicates with its own set of Azure IoT Hub instances to automatically validate against devices to Azure IoT Hub bi-directional connectivity and other IoT Hub primitives.

Previously, hardware manufacturers had to instantiate their own IoT Hub using their Azure subscription in order to get certified. AICS not only eliminates Azure subscription costs for our hardware manufacturers, but also streamlines the certification processes through automation. These changes accrue to driving more quality and consistency compared to the manual processes that were in place before.

Additional tests

The certification program for IoT devices has always validated against bi-directional connectivity from device to IoT Hub cloud service (namely device-to-cloud and cloud-to-device). As IoT devices become more intelligent to support more capabilities, we have now expanded our program to support validation of device twins and direct methods IoT Hub primitives. AICS validate these capabilities and Azure IoT device catalog will correspondingly showcased them as well that make it easy for device seekers to build IoT solutions on these rich capabilities.

The screenshot below shows customizable test cases. By default, device-to-cloud is the required test and all others are optional. This new requirement allows constrained devices such as microcontrollers to be certified.

The screenshot below shows how tested capabilities are shown on the device description page in the device catalog.

Flexibility

Previously, hardware manufacturers were required to use the Azure IoT device SDK to build an app to establish connectivity from device(s) to cloud managed by Azure IoT Hub services. Based on partners’ feedback, we now support devices that do not use Azure IoT device SDK to establish connectivity to Azure IoT Hub, for example, devices that use the IoT Hub resource provider REST API to create and manage Azure Hub programmatically or hardware manufacturers opt to use other device SDK equivalent to establish connectivity.

In addition, AICS allows hardware manufacturers to configure the necessary parameters for customized test cases such as number of messages of telemetry data sent from the devices.

The screenshot below illustrates an example page that shows the ability to configure each test case.

Simplicity

Finally, we have made investments to design a user experience that is simple and intuitive to hardware manufacturers. For example, in the device catalog, we have streamlined the process from device registration to running the validations using AICS through a simple wizard driven flow. Hardware developers can easily troubleshoot failed tests through detailed logs that improves diagnose-ability.

Because it’s a web-based workflow, serviceability of AICS is so simple that hardware manufacturers are not required to deploy any standalone test kits (no .exe, .msi, etc.) locally on their devices, which tend to become outdated over time.

The screenshot below shows each test case run. Log files show the test pass/fail along with raw data sent from device to cloud. The submit button only shows up when all the test cases selected pass. Once the tests are complete, we will review the results and notify the submitter of additional steps to complete the entire certification process.

Next steps

Go to Partner Dashboard to start your submission.

Effective immediately, all new incoming submissions for certification must be validated via AICS. We also highly recommend that existing certified IoT devices re-certify using AICS because doing so allows us to showcase your additional hardware capabilities.

You can learn more about AICS in this demo video.

If you have any questions, please contact Azure Certified for IoT iotcert@microsoft.com.
Quelle: Azure

Creating a smart grid with technology and people

This blog post was authored by Peter Cooper, Senior Product Manager, Microsoft IoT.

It’s 1882. Thomas Edison has just surpassed his breakthrough invention—the first incandescent lightbulb—by collaborating with J.P. Morgan to open the first industrial-scale power station in the United States.

Flash forward to today: Power generation, distribution, transmission, and consumption now drive business and modern life around the globe. The industry operates on a vast scale, with a complex web of relationships and technology that enables instant, reliable delivery throughout much of the developed world.

And that grid that got its start back in the 19th century? It’s sorely in need of a massive update. Utilities and their partners are searching for new solutions that can meet 21st-century energy challenges: surging demand for electricity, two-way energy flow, increased use of clean energy sources, and stairstep approaches to creating a smart grid to tackle the thorniest challenges first. Here’s a look at the digital transformation of the power and utilities industry that is picking up steam.

The need to make electrical power more sustainable

The current model of power production and delivery won’t sustain fast-paced business and human population growth. Power systems are already coping with spikes, surges, and even blackouts. Who can forget the Northeast blackout of 2003?

Moreover, the existing grid is wasteful, with 285 percent more power loss today than in 1984. Such inefficiency has highly negative consequences for consumers’ need for reliability, climate change, and businesses’ bottom lines.

A 150-year-old industry goes high tech

Fortunately, new technologies are emerging to manage demand, reduce waste, and harness new energy sources and producers. Here are just a few:

Smart meters that communicate their condition via wireless networks, providing consumers with real-time data and aiding in faster resolution of power disruption issues.
Connected home systems that use sensor-tagged equipment and AI-powered smart assistants to fine-tune energy use throughout the house, even achieving “zero net” energy use.
State-of-the-art batteries that store energy, for future use or sale back to the grid.
Microgrids that combine solar panels, fuel cells, and battery energy storage to power neighborhoods.
Connected cars that reduce energy use, can be charged systematically, and serve as movable energy storage devices.
Next-generation distribution and transmission infrastructures that will enable two-way power flow.
The smart grid, which combines multiple innovations to enable systematic load balancing, peak leveling programs, and full leverage of sustainable energy sources.

All of these developments—and more—are making it possible to deliver electricity to the right customer at the right time and in the right manner. Power companies now also can accommodate the two-way flow of energy, as grassroots producers, both businesses and individuals, deploy their own small-scale energy production. These capabilities are being amplified by a new IoT platform, Azure Digital Twins, that uses spatial intelligence to model complex relationships between people, places, and devices in the energy value chain. Let’s take a closer look.

A grid made smarter by digital technology

Without question, the legacy grid needs to be modernized with state-of-the-art infrastructure to improve effectiveness and ensure a reliable flow of continuous power. Creating a smart grid is slated to cost between $476 and $880 billion, and it will take years to achieve. But new Internet of Things (IoT) digital technology can “smarten” today’s grid faster and at a lower cost. It can also connect all the players—electricity producers, customers, and transmission and distribution companies—providing continuous feedback to help them make more sustainable choices now.

Agder Energi, a hydropower company in Norway, already has used sensor-linked equipment and Microsoft technology, including Microsoft Azure, Power BI, and Azure IoT Hub, to improve energy forecasting, adapt energy production to changing needs, and empower consumers with insights to manage their energy usage. Now, with Azure Digital Twins, Agder Energi is taking those capabilities to a new level. The technology enables Agder Energi to model grid assets and distributed energy resources and optimize them where needed.

Why is this important? Azure IoT enables power companies like Agder Energi to rapidly identify and address sources of waste, right-size production, prioritize investments, and incorporate new energy producers and sources. For example, if a power company finds that a substation is a major source of energy leakage, the company could fast-track upgrades. Or if demand unexpectedly surges, the power company may elect to add more resources, such as wind or solar energy, to ensure continuous electrical delivery.

The rise of new energy “prosumers”

Where will generation companies harness new energy sources? Meet the new prosumers: businesses and individuals who are both consumers and producers of energy.

Businesses may elect to lease land to a wind farm, use solar panels across company buildings, or run fleets of electric vehicles that both use and store energy. Similarly, consumers are increasingly buying solar panels and electric cars to be more sustainable, as well as using smart meters and analytics to monitor and reduce consumption. Both of these groups are likely to store and sell excess energy back to the grid. While in its infancy, the prosumer market is expected to take off in the near future. Mass adoption of autonomous cars could really galvanize this movement.

Allego is a European provider of smart charging solutions and electric vehicle cloud solutions in Europe. The company uses Azure IoT to model all key participants in the charging network, such as regions, charging stations, vehicles, and others to simplify the business complexity of planning and executing charging. The solution enables charging stations to more precisely plan energy delivery, prioritize public vehicles such as buses over others, and charge consumer vehicles according to driver preference, among other benefits.

Smart grid technology means new choices

In the very near future, power generation companies will have greater options in how they run their businesses, using IoT-enabled insights to strategically stairstep their way to creating a smart grid and ensure business continuity. Meanwhile, prosumers will be able to align their values and behavior and benefit financially from sustainable choices, encouraging others to do the same.

Learn about Microsoft’s work on sustainable energy management.
Quelle: Azure

Azure Marketplace new offers – Volume 27

We continue to expand the Azure Marketplace ecosystem. From November 1 to November 16, 2018, 61 new offers successfully met the onboarding criteria and went live. See details of the new offers below:

Virtual machines

CIS Ubuntu Linux 18.04 LTS Benchmark L1: This image of Ubuntu Linux 18.04 is preconfigured by CIS to the recommendations in the associated CIS Benchmark. CIS Benchmarks are vendor-agnostic, consensus-based security configuration guides.

Couchbase Enterprise: Modernize your technology environment with Couchbase's full-featured engagement database. It's adaptive, responsive, scalable, intelligent, highly available, and easy to manage.

CyberPosture Intelligence 1-Month – Trial: Cavirin CyberPosture Intelligence combines automated discovery, monitoring, infrastructure risk scoring, and auto-remediation to help organizations of all sizes leverage the cost savings and agility of the cloud.

CyberPosture Intelligence Annual BYOL: Cavirin CyberPosture Intelligence combines automated discovery, monitoring, infrastructure risk scoring, and auto-remediation to help organizations of all sizes leverage the cost savings and agility of the cloud.

FlashGrid SkyCluster for Oracle RAC: FlashGrid SkyCluster is an engineered cloud system for database high availability. SkyCluster comes as a fully integrated Infrastructure-as-Code template that you can customize and deploy to your IaaS cloud account with a few clicks.

Flexify.IO – Azure Blob/Amazon S3 Data Migration: Migrate data between Azure Blob storage, Amazon S3, and Google Cloud Storage accounts. Flexify.IO validates checksums at every step of migration and retries errors, ensuring your data arrives the same as it was at the source.

Flexify.IO – Multi-Cloud Storage: Flexify.IO enables cloud-agnostic and multi-cloud storage deployments by combining Azure Blob storage, Amazon S3, Google Cloud Storage, and others into a single virtual storage repository and making it available via the S3 API.

Oncore!DOCS, Powered by Nextcloud: Oncore!DOCS is an open-source file sync and share solution designed to be easy to use. It's powered by Nextcloud and optimized for Microsoft Azure.

ont_dev_platform: This is a decentralized application development platform. Users and developers can develop, compile, deploy, and invoke intelligent contracts using a mirror platform composed of Ontology, SmartX (IDE), and Explorer.

Panzura Freedom CloudFS 7.2.2.0 (EARLY ACCESS): New features in 7.2.2.0 include support for virtual NIC and multibyte character support, enabling long path and file names for Japanese and Chinese characters.

Phishing Frenzy on Ubuntu Server: Phishing Frenzy is a software tool for penetration testers that’s built with the Ruby on Rails web-application framework.

RSA NetWitness Platform 11.2: RSA NetWitness Platform v11.2 adds a new User and Entity Behavior Analytics (UEBA) offering, with increased capabilities for detection, response, and forensics activities, as well as added contextual information from RSA Archer.

Scai Analytics & Database Management Web Platform: Scai is a business intelligence and database management web platform for SQL databases and Azure SQL Database. Scai was built for companies that need a powerful, affordable, simple analytics and management tool.

scalearc: The ScaleArc database load balancing software enables an agile data tier that eliminates planned and unplanned downtime, enables failover that avoids application disruption, and delivers instant scalability with no changes to the app or database.

Serverless360: Serverless360 offers powerful message processing for real-time business requirements, integration with major external notification channels, and extensive serverless monitoring for a complete integration solution.

SFTP Secure Server Windows 2016 OpenSSH: This solution is an FTP/FTPS/SFTP server that enables users to access remote files over TCP/IP networks, such as the internet. Unlike FTP, the FTPS and SFTP protocols provide security and strong data encryption.

Topicus KeyHub: Topicus KeyHub provides a complete access management solution for team-based single sign-on, real-time provisioning, and password management.

Ubuntu Server 16.04 LTS + Azure IoT Edge runtime: Azure IoT Edge is a fully managed service that delivers cloud intelligence locally by deploying and running artificial intelligence, Azure services, and custom logic directly on cross-platform IoT devices.

VisualBase: VisualBase is a dynamic business application platform with powerful development tools at runtime. Organizations turn to VisualBase for its capability, flexibility, and reliability.

VisualRM: VisualRM is a risk management tool that empowers organizations to identify, score, categorize, classify, and rank risks, then put in place short-term and long-term mitigation actions and plans.

Web applications

Couchbase Server Enterprise Container: Modernize your technology environment with Couchbase Server Enterprise Container. Built on powerful NoSQL technology, Couchbase Server gives you the flexibility to constantly reinvent the customer experience.

DigiCert Code-Signing Certificates Listing: Code-signing certificates are used by software developers to digitally sign apps, drivers, and software programs as a way for end users to verify that the code they receive has not been altered or compromised by a third party.

Exact Lightweight Integration Server: Exact Lightweight Integration Server offers a central management console to install, update, manage, and monitor all data integrations within your Exact environment.

QuotaGuard Static IPs: QuotaGuard Static IPs are enterprise-ready inbound/outbound proxied static IP services complete with dual-static IP provisions, health monitoring, load balancing, and automated failover for each account.

Container solutions

Git Container Image: Git is an open-source distributed version control system that can handle both small and large projects with speed and efficiency.

Consulting services

1 Week CIO Assessment: CrucialLogics will perform an assessment to determine the current IT landscape and generate an executive deliverable with action plans and next steps for the IT road map on your Microsoft platform (Office 365, Azure, Dynamics).

Accelerating Digital Transformation Assessment: In this one-day workshop, sopra steria will assess your technology and application transformational journey in line with your business strategy, then provide a report advising either rebuilding, rehosting, or refactoring your apps.

Azure & Microsoft 365 Security:2-Wk Implementation: Protect your business data, operational infrastructure, and business identity against cyber threats and data breaches with Steeves and Associates’ Enhanced Security + Data Protection Service for Microsoft 365 and Azure.

Azure Backup and Disaster Recovery:1-Hour Briefing: Disasters can happen to any business. In this briefing by Communication Square, you'll learn how you can leverage Azure Backup and Disaster Recovery options to ensure your business keeps progressing.

Azure Backup: 2-Week POC: Get a portion of your workload backed up on Azure and see all the tools and features in action in this proof of concept from Communication Square. Azure Backup enables you to secure your data in the cloud without the extra costs of infrastructure.

Azure Backup: 4-Week Implementation: In this four-week implementation, Communication Square will back up your workload with Azure Backup. Experience lower operational costs, added security, and pay-as-you-go storage.

Azure Data Center Migration: 4 Week Implementation: Spend less and achieve more by moving your datacenter to the cloud. Migrate your data to Azure with the help of Communication Square's Microsoft-certified experts.

Azure Datapath 10-Weeks Implementation: A challenge for many organizations on their cloud journey is how to efficiently migrate large amounts of data to Azure. Azure Datapath is a service provided by Servent to help accelerate and expedite your migration.

Azure Disaster Recovery: 2-Week POC: With Azure Disaster Recovery, you can create a customized plan to keep your business running in any situation. Get a portion of your workload replicated on Azure in this proof of concept from Communication Square.

Azure Disaster Recovery: 4-Week Implementation: In this four-week implementation by Communication Square, you can get your workload replicated on Azure and receive a comprehensive disaster recovery plan for the continuity of your business.

Azure Done Right Migration: Developed by Netsurit, Azure Done Right is a predefined set of planning and migration procedures and tools to assist you in the successful migration of workloads to Microsoft Azure.

Azure Pathway 10-Weeks Implementation: Azure Pathway by Servent helps simplify your journey to Microsoft Azure. Accelerate your migration with step-by-step guidance and technical resources customized for your workloads.

Backup and Recovery: 5-Day POC: In this engagement from Catapult Systems, customers will learn cloud backup best practices and receive a proof of concept that sends two workloads into Azure Backup.

Cloud Coaching Service 8 Week Workshop: This offer from Compositional IT will deliver one-hour coaching sessions with your developers to help keep your cloud applications on the right track.

Cloud Tech Accelerator 5 Day Proof of Concept: Compositional IT's Tech Accelerator is designed to let us solve those specific or edge-case technology problems that would be prohibitively expensive or time-consuming for you to solve yourself.

Database DevOps Jumpstart: 2-Wk Proof of Concept: DevOps unifies software development and software operation, enabling shorter development cycles and increased deployment frequency. This proof of concept from Coeo will reveal the benefits of Azure DevOps Services.

DB Shield-DB Compliance. 4 week Implementation: The DB Shield Azure service protects databases with a set of preconfigured defenses and helps build a custom security policy for your environment.

DevOps Pipeline Assessment: 3 Day Assessment: This assessment by DevOpsGroup provides an in-depth analysis of your automation technology to create best-practice continuous integration/continuous deployment (CI/CD) pipelines.

DevOps Transformation Discovery: 3 Day Assessment: This assessment by DevOpsGroup will help you understand operational and technical challenges, explore technological or policy-related constraints, and establish the needs of your organization.

DevOps with Azure Automation Jumpstart: 2-Wk PoC: Implement DevOps practices to improve agility, collaboration, and productivity in this two-week proof of concept from Coeo.

Empowering Remote Workers: 1 Hour Briefing: In this free briefing, you’ll learn how Communication Square can help you secure devices and protect sensitive data using Microsoft Azure. Empower your remote workforce with secure access to your organization’s data.

Empowering Remote Workers: 4-Week Implementation: Communication Square will meet with you to determine your deployment goals and go over use-case scenarios, then implement Microsoft Intune for mobile device management, with testing and validation.

Enterprise Blockchain Discovery: 1-Day Assessment: In this free assessment from Envision Blockchain Solutions, participants will learn the benefits and values of conducting their business with blockchain solutions.

Envision Workshop SAP on Azure: Cooperation between Microsoft and SAP facilitates the efficient and secure migration of central IT processes to the cloud for business customers. This workshop by SYCOR will detail the benefits of SAP on Azure.

GDPR Compliant Cloud Solutions: 1-Hour Briefing: In this one-hour briefing by Communication Square, learn about GDPR compliance and how to quickly and cost-effectively achieve it. Microsoft Compliance Manager and other Microsoft services will be discussed.

GDPR Compliant Cloud Solutions: 4-Week Imp.: Communication Square will help your organization achieve GDPR compliance, and you'll receive Microsoft Compliance Manager training and insights on data protection capabilities.

Modern Workplace Enablement: 4-Week Implementation: Increase productivity and empower your workforce with the latest collaboration and productivity tools. Communication Square will take care of all aspects of the modern workplace enablement program.

Modern Workplace for Firstline Workers: 4-Wk Imp.: This implementation from Communication Square will help you digitize work, modernize teamwork, and improve security.

Modern Workplace: 1-Hour briefing: Do you want to retire paper and pencil? Do you want a better way to communicate with your team members? Communication Square's one-hour briefing will help you understand what you can achieve and optimize with Microsoft Azure.

SAP Azure 10-Week Implementation: SAP Azure from Servent is our framework to help you migrate SAP to Azure. With this offering, we simplify the deployment and migration process and enable your journey to use SAP on Azure.

Secure Azure 10-Weeks Implementation: The Secure Azure implementation by Servent is a comprehensive set of security solutions. These solutions ensure that when you migrate workloads to Azure, they will be secured following Microsoft and industry standards.

Secure your Data implementation: At Netsurit, our Secure Your Data offer mitigates any threats by implementing effective and proven measures to ensure your data is protected at all times.

Secure your Devices implementation: Netsurit’s team of experts will work with you to create a customized plan to implement tools and processes so you can effectively manage and secure your company devices.

Secure your Identity Implementation: Netsurit’s Secure Your Identity solution enables automated user lifecycle management across HR systems both on-premises and in the cloud.

Setup SentryOne Monitoring Software monitor 5 Svrs: In this implementation, Denny Cherry & Associates Consulting will set up SentryOne monitoring software on a repository server and configure monitoring on up to five servers.

TFS to Azure DevOps Migration: 2 weeks: In this two-week implementation, DevOpsGroup will migrate your current work items, history, and code repository from Team Foundation Version Control (TFVC) to Git for source control.

Quelle: Azure

Static websites on Azure Storage now generally available

Today we are excited to announce the general availability of static websites on Azure Storage, which is now available in all public cloud regions.

What is a static website?

Static websites refer to websites that can be loaded and served statically from a pre-defined set of files. You can now build a static website using HTML, CSS, and JavaScript files that are hosted on Azure Storage. In contrast, if you want to host a dynamic website with the ASP.NET, Java, or Node runtime, use Azure Web Apps and rely on the runtime to generate and serve your web content dynamically.

Static websites can be powerful with the use of client-side JavaScript. You can build a web app using popular frameworks like React.js and Angular and host it on Azure Blob storage. If there is a need to manipulate or process data on the server side, simply call the relevant managed Azure service like Azure Cognitive Services or leverage a web server of your own hosted on Azure Functions.

Get started now

Azure Storage makes hosting of websites easy and cost-efficient. When you enable the static website hosting on your Azure Storage account, a container named ‘$web’ is automatically created for you. You can then upload your static content to this container for hosting. Your content will be available through a web endpoint (i.e., myaccount.z20.web.core.windows.net) and will get a default page and a 404 page of your choice.

You can enable static website hosting using the Azure portal, Azure CLI, or Azure PowerShell. If you prefer a guided experience, follow the tutorial series on hosting your website on Azure Storage and configuring a custom domain with an SSL certificate.

A sample website – your own file browser for Azure Storage

One scenario where you might use static website hosting is to build a website to interact with your data in Azure Storage. You can secure your data by protecting your files via RBAC roles and Azure Active Directory authentication, and manipulate the data using the Azure JavaScript SDKs. This example uses the new Azure Storage SDK for JS to list files in Blob storage and render in a file browser in a statically hosted website. The example uses anonymous authentication to interact with Azure Blob storage, but you can also use Azure AD authentication to restrict access to your data.

Get the sample on GitHub, and try the demo.

There are many other use cases for static websites with today’s distributed architectures. A common use is building a serverless application in the cloud and creating a front end for it using a static website. Watch our photo gallery demo, “Serverless compute architectures with Azure Blob Storage,” from Ignite 2018, or follow the tutorial, “Build a serverless web app in Azure,” to learn more about building serverless architectures fronted with a static website.

Pricing

There are no additional charges for enabling static websites on Azure Storage. The pricing for storing and accessing your data will apply and can be viewed on the pricing page. In addition to the storage costs, data egress charges will apply and are described within the Bandwidth Pricing Details.

Additionally, you might want to enable Azure CDN to use a custom domain with an SSL certificate, as well as making use of features like custom rewrite rules. If you do so, Azure CDN charges will apply, and may lower your costs depending on your usage pattern.

Feedback

Thank you to everyone who participated in the preview of the static website feature. In the upcoming months we plan to make many enhancements to the feature based on your feedback. Continue providing feedback by posting on Azure Feedback.
Quelle: Azure