Juni 2019 - Seite 15 von 73 - Cloud Computing Köln

News and updates

Announcing native backup for SQL Server 2008 end of support in Azure

With SQL Server 2008 and 2008 R2 approaching end of support, many customers are moving to Azure. They see this milestone as an opportunity to reimagine and transform their infrastructure with the power of cloud computing. Azure’s offer of free extended security updates for three years provides a new lease on life to these servers while giving organizations time to upgrade. Learn how easy it is to protect your SQL databases in Azure.

Microsoft and Truffle partner to bring a world-class experience to blockchain developers

Last month, Microsoft released Azure Blockchain Service making it easy for anyone to quickly setup and manage a blockchain network and providing a foundation for developers to build a new class of multi-party blockchain applications in the cloud. To enable end-to-end development of these new apps, we’ve collaborated with teams from Visual Studio Code to Azure Logic Apps and Microsoft Flow to Azure DevOps, to deliver a high-quality experience that integrates Microsoft tools developers trust and open-source tools they love. Now we have doubled down on our relationship by announcing an official partnership between our organizations to bring Truffle blockchain tools for developer experience and DevOps to Microsoft Azure.

Now available

Azure and Office 365 generally available today, Dynamics 365 and Power Platform available by end of 2019

Microsoft Azure and Microsoft Office 365 are taking a major step together to help support the digital transformation of our customers. Both Azure and Office 365 are now generally available from our first cloud datacenter regions in the Middle East, located in the United Arab Emirates (UAE). Dynamics 365 and Power Platform, offering the next generation of intelligent business applications and tools, are anticipated to be available from the cloud regions in UAE by the end of 2019.

In preview

Introducing next generation reading with Immersive Reader, a new Azure Cognitive Service

We’re unveiling the preview of Immersive Reader, a new Azure Cognitive Service in the Language category. Developers can now use this service to embed inclusive capabilities into their apps for enhancing text reading and comprehension for users regardless of age or ability. No machine learning expertise is required. Based on extensive research on inclusivity and accessibility, Immersive Reader’s features are designed to read the text aloud, translate, focus user attention, and much more. Immersive Reader helps users unlock knowledge from text and achieve gains in the classroom and office.

Announcing the preview of Microsoft Azure Bastion

For many customers around the world, securely connecting from the outside to workloads and virtual machines on private networks can be challenging. Exposing virtual machines to the public Internet to enable connectivity through Remote Desktop Protocol (RDP) and Secure Shell (SSH), increases the perimeter, rendering your critical networks and attached virtual machines more open and harder to manage. To connect to their virtual machines, most customers either expose their virtual machines to the public Internet or deploy a bastion host, such as jump-server or jump-boxes. So we’re excited to announce the preview of Azure Bastion, a new managed PaaS service that provides seamless RDP and SSH connectivity to your virtual machines over the Secure Sockets Layer (SSL).

Virtual machine scale set insights from Azure Monitor

In October 2018 we announced the public preview of Azure Monitor for Virtual Machines (VMs). At that time, we included support for monitoring your virtual machine scale sets from the at scale view under Azure Monitor. Now Today we are announcing the public preview of monitoring your Windows and Linux VM scale sets from within the scale set resource blade. This blog highlights several enhancements.

Technical content

Using Azure Search custom skills to create personalized job recommendations

The Microsoft Worldwide Learning Innovation lab is an idea incubation lab within Microsoft that focuses on developing personalized learning and career experiences. One of the recent experiences that the lab developed focused on offering skills-based personalized job recommendations. Research shows that job search is one of the most stressful times in someone’s life. Everyone remembers at some point looking for their next career move and how stressful it was to find a job that aligns with their various skills. Harnessing Azure Search custom skills together with our library of technical capabilities, we were able to build a feature that offers personalized job recommendations based on identified capabilities from resumes.

Azure Stack IaaS – part ten

One of the best things about running your VMs in Azure or Azure Stack is you can begin to modernize around your virtual machines (VMs) by taking advantage of the services provided by the cloud. Platform as a Service (PaaS) is the term often applied to the capabilities that are available to your application to use without the burden of building and maintaining these capabilities yourself. Actually, cloud-IaaS itself is a PaaS since you do not have to build or maintain the underlying hypervisors, software defined network and storage, or even the self-service API and portal. Furthermore, Azure and Azure Stack gives you PaaS services which you can use to modernize your application. In this article we will explore how you can modernize your application with web apps, serverless functions, blob storage, and Kubernetes as part of your Journey to PaaS.

Getting Started with Azure Machine Learning service with Visual Studio Code | Azure Tips and Tricks

In Azure, you can create complex machine learning models and train them with data in a Machine Learning Service workspace. This is a workspace where you can manage all of your machine learning tools and assets, like experiments, models, scripts and model deployments. And you can use the workspace to share your machine learning work with other data scientists in your team. In the Machine Learning Service workspace, you can. Let’s get started with Azure Machine Learning for VS Code and the Azure Machine Learning Service works

Azure Shows

Azure tips and tricks for Visual Studio 2019 | Azure Friday

Learn Michael Crump’s latest Azure tips and tricks that will help you be more productive working with Azure in Visual Studio 2019.

.NET Core 3.0 with Scott Hunter | On .NET

.NET Core 3 will be a major milestone with tons of new features, performance updates and support for new workloads. In this episode, Richard Lander and Scott Hunter get together to discuss some of the highlights that developers can look forward to in this new release.

Server-side Blazor in .NET Core 3.0 | On .NET

In this episode, Shayne Boyer sits down with Daniel Roth to get an understanding of what Blazor is and what benefits does it bring to the table for building web applications.

Five things you didn’t know Python could do | Five Things

This week, Python (the language, not the snake) aficionado Nina Zakharenko joins us for Five Things that you didn’t know that Python can do. And don’t worry, there are plenty of snake references and even a free potato joke. Also, Burke finds snake facts on the internet and Nina tries her first Goo Goo Cluster.

All about Rust in real life: Linkerd 2.0 | The Open Source Show

Oliver Gould, CTO at Buoyant and one of the creators of Linkerd, joins Lachie Evenson to talk Rust. One of StackOverflow’s most loved programming languages for the fourth year running. Specifically, how and why Linkerd rewrote 2.0 in Rust, what’s changed over the years, and get Oliver’s tips for navigating tooling, package management, release channels, and more.

Azure IoT Edge development with Azure DevOps | Internet of Things Show

The Internet of Things is a technology paradigm that involves the use of internet connected devices to publish data often in conjunction with real-time data processing, machine learning, and/or storage services. We will examine IoT Edge Solutions using Azure DevOps, Application Insights, Azure Container Registries, containerized IoT edge devices and Azure Kubernetes Service to create an end-to-end pipeline which deploys, smoke tests, and allows for scalable integration testing using replica sets in k8s.

Eric Fleming on middle-of-the-day deployments | Azure DevOps podcast

Today’s episode is all about recognizing middle-of-the-day deployments. How teams such as Netflix, Facebook, and even the Azure DevOps Product Team are doing them; and taking a look at how other teams can achieve that for themselves!

Quelle: Azure

25. Juni 2019

da Agency

Analyze BigQuery data with Kaggle Kernels notebooks

We’re happy to announce that Kaggle is now integrated into BigQuery, Google Cloud’s enterprise cloud data warehouse. This integration means that BigQuery users can execute super-fast SQL queries, train machine learning models in SQL, and analyze them using Kernels, Kaggle’s free hosted Jupyter notebooks environment.Using BigQuery and Kaggle Kernels together, you can use an intuitive development environment to query BigQuery data and do machine learning without having to move or download the data. Once your Google Cloud account is linked to a Kernels notebook or script, you can compose queries directly in the notebook using the BigQuery API Client library, run it against BigQuery, and do almost any kind of analysis from there with the data. For example, you can import the latest data science libraries like Matplotlib, scikit-learn, and XGBoost to visualize results or train state-of-the-art machine learning models. Even better, take advantage of Kernel’s generous free compute that includes GPUs, up to 16GB of RAM and nine hours of execution time. Check out Kaggle’s documentation to learn more about the functionality Kernels offers.With more than 3 million users, Kaggle is where the world’s largest online community of data scientists come together to explore, analyze, and share their data science work. You can quickly start coding by spinning up a Python or R Kernels notebook, or find inspiration by viewing more than 200,000 public Kernels written by others.For BigQuery users, the most distinctive benefit is that there is now a widely used Integrated Development Environment (IDE)—Kaggle Kernels—that can hold your querying and data analysis all in one place. This turns a data analyst’s fragmented workflow into a more seamless process instead of the previous way, where you would first query data in the query editor, then export the data elsewhere to complete analysis. In addition, Kaggle is a sharing platform that lets you easily make your Kernels public. Kaggle lets you disseminate your open-source work and also discuss data science with the world’s top-notch data scientist professionals.Getting started with Kaggle and BigQueryTo get started with BigQuery for the first time, enable your account under the BigQuery sandbox, which provides up to 10GB of free storage, 1 terabyte per month of query processing, and 10GB of BigQuery ML model creation queries. (Find more details on tier pricing in BigQuery’s documentation).To start analyzing your BigQuery datasets in Kernels, sign up for a Kaggle account. Once you’re signed in, click on “Kernels” in the top bar, followed by “New kernel” to immediately spin up your new IDE session. Kaggle offers Kernels in two types: scripts and notebooks. For this example, the notebooks option is selected.In the Kernels editor environment, link your BigQuery account to your Kaggle account by clicking “BigQuery” on the right-hand sidebar, then click “Link an account.” Once your account is linked, you can access your own BigQuery datasets using the BigQuery API Client library.Let’s try this out using the Ames Housing dataset that’s publicly available on Kaggle. This dataset contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, as well as their final sales price. Let’s compose a query to gain some insights from the data. We want to find out what different home types there are in this dataset, as well as how many do or do not have central air conditioning installed. Here’s how the query looks:We quickly get a response showing that one-story homes are the most common home style in Ames and that, regardless of home style, most homes have central air conditioning. There are many more public datasets on Kaggle that you can explore in this way.Building ML models using SQL queriesAside from data analysis, BigQuery ML lets you create and evaluate machine learning models using SQL queries. With a few queries, any data scientist can build and evaluate regression models without extensive knowledge of machine learning frameworks or programming languages. Let’s create a linear model that aims to predict the final sales price of real estate in Ames. This model will train on a couple inputs—living area size, year built, overall condition, and overall quality. Here’s the model code:In just one query, we’ve created a SQL-based ML model inside Kernels. You could continue using Kernels to create more advanced queries for analysis and optimize your model for better results. You may even choose to publish your Kernel to share publicly with the Kaggle community and broader Internet after your analysis is complete. To see the rest of the workflow on obtaining training statistics and evaluating the model, visit the complete How to use BigQuery on Kaggle tutorial. This tutorial is publicly available as a Kernels notebook. You can also check out the Getting started with BigQuery ML Kernel that goes into greater depth on training and evaluating models.Learn more details on navigating the integration by visiting Kaggle’s documentation. Also, sign up for Kaggle’s new and updated SQL micro-course that teaches you all the basics of the SQL language using BigQuery. We hope you enjoy using this integration!
Quelle: Google Cloud Platform