Juni 2019 - Seite 30 von 73 - Cloud Computing Köln

A vast majority of financial institutions are exploring cloud computing to see how they might serve investment processes and customers better, faster and more securely, and be able to plan for the future with modern infrastructure. Crux Informatics, a data delivery and operations company, built its infrastructure from the ground up with Google Cloud Platform (GCP), and hasn’t looked back. We’re pleased to partner with Crux Informatics to help scale the building of secure, efficient and high-quality data pipelines for the financial services industry.Since its inception, Crux has used its cloud platform and expert services to connect data users and suppliers in the capital markets so data can flow. Data suppliers are large data owners and distributors that gather and send raw financial data to banks and hedge funds—the data users—for them to make trading and other critical business decisions. These data suppliers provide a wide variety of data sets in multiple formats to users. Until recently, financial firms ingested and processed data in-house. As a result, their data analysts spent a significant amount of time and resources focusing on the repetitive tasks of extracting, cleaning and validating the data to prepare it for analysis.Crux eliminates the need for financial firms to build and maintain these complex data management infrastructures in-house by providing a solution that replaces the data supply chain ingestion process. Its cloud-based delivery and operations platform serves as an industry utility, so customers can reliably process the data they need, when they need it and where they need it. In turn, data users can access more data from their suppliers and build their own proprietary services, moving away from repetitive tasks to sophisticated data analysis that brings knowledge and insight to capital markets.Cloud benefits spread to the financial services industryFinancial institutions have uncompromising technology requirements regarding security, compliance and accuracy. With persistent and rapid change within financial services, it’s essential that technology keeps up the same pace. But maintaining massive repositories of data in-house makes it harder for financial firms to move quickly. Migrating legacy systems into a streamlined data supply chain on the cloud can also be very challenging. For Crux’s customers, their product brings a simpler way to ease into cloud use, rather than having to choose separate services.Crux uses a variety of GCP’s set of tools and services to help build its business faster and serve its customers better. The cloud-native approach that Crux has taken means that it can offer high resiliency by using multi-region storage buckets in Cloud Storage combined with GCP’s global network and ability to spin up high-availability clusters. Crux is able to detect and respond quickly to issues thanks to its comprehensive monitoring, logging, and tracing infrastructure that uses Stackdriver.High-performance data access and data processing are foundational aspects of the Crux offering, which is facilitated by Google Kubernetes Engine (GKE), BigQuery for data analytics and Istio for production monitoring. Security is also paramount for Crux, and GCP’s Shared VPC option gives the company a global IP space where firewall rules for multiple projects can be centrally managed by the infrastructure team.These GCP services have helped Crux grow and scale up quickly, and has led to many benefits for Crux’s customers. One financial institution had only been able to manually ingest 10 data sets a month from a specific supplier, limited by their in-house capacity and resources. With Crux and GCP, that user can now consume more than 50 data sets from the supplier immediately, as the data sets are processed and loaded for consumption.Crux plans more growth and development to serve even more clean, useful data to its customers. Learn more about Crux and about GCP for financial services.
Quelle: Google Cloud Platform

19. Juni 2019

da Agency

Predictive marketing analytics using BigQuery ML machine learning templates

Enterprises are collecting and generating more data than ever—to better understand their business landscape, their market, and their customers. As a result, data scientists and analysts increasingly need to build robust machine learning models that can forecast business trajectories and help leaders plan for the future. However, current machine learning tools make it difficult to quickly and easily create ML models, delaying time to insights.To address these challenges, we announced BigQuery ML, a capability inside BigQuery that allows data scientists and analysts to build and operationalize machine learning models in minutes on massive structured or semi-structured datasets. BigQuery ML democratizes predictive analytics so that users unfamiliar with programming languages like Python and Java can build machine learning models with basic SQL, and is generally available.To make it even easier for anyone to get started with BigQuery ML, we have open-sourced a repository of SQL templates for common machine learning use cases. The first of these, tailored specifically for marketing, were built in collaboration with SpringML, a premier Google Cloud Platform partner that helps customers successfully deploy BigQuery and BigQuery ML. Each template is tutorial-like in nature, and includes a sample dataset for Google Analytics 360 and CRM along with SQL code for the following steps of machine learning modeling: data aggregation and transformation (for feature and label creation), machine learning model creation, and surfacing predictions from the model on a dashboard. Here’s more on the three templates:Customer segmentation—By dividing a customer base into groups of individuals that are similar in specific ways, marketers can custom-tailor their content and media to unique audiences. With this template, users can implement a BigQuery ML k-means clustering model to build customer segmentations.Customer Lifetime Value (LTV) prediction—Many organizations need to identify and prioritize customer segments that are most valuable to the company. To do this, LTV can be an important metric that measures the total revenue reasonably expected from a customer. This template implements a BigQuery ML multiclass logistic regression model to predict the LTV of a customer to be high, medium, or low.Conversion or purchase prediction—There are many marketing use cases that can benefit from predicting the likelihood of a user converting, or making a purchase, for example ads retargeting, where the advertiser can bid higher for website visitors that have a higher purchase intent, or email campaigns, where emails are sent to a subset of customers based on their likelihood to click on content or purchase. This template implements a BigQuery ML binary logistic regression model to build conversion or purchase predictions.To start using these open-source SQL templates and more, visit our repository—the code is licensed under Apache v2. We will also be adding templates for more use cases in the future. And to learn more about applying BigQuery ML for marketing analytics, watch this Google Cloud OnAir webinar.
Quelle: Google Cloud Platform

19. Juni 2019

da Agency

A little light reading: New, interesting and hands-on stories from around Google

There’s much more going on in the wide world of Google than cloud computing alone, so we’ve rounded up some recent favorite stories to share with you. Take a look at what’s happening in our developer community and in the AI lab, and find some projects to tackle for fun and skill-building.Build a machine learning model (in less than an hour)If you’re interested in AI and machine learning, but haven’t dived into the details yet, check out this session from Google I/O ‘19, where developer advocate Sara Robinson built an AI model from scratch on stage. The talk is intended for ML beginners, experts, and anyone in between. You’ll get a brief high-level overview of what ML is (essentially, matrix multiplication), and what types of Google products can help you add ML to your apps. The session will take you through coding, training, and deploying a model using a public BigQuery dataset of StackOverflow questions.Let your code fly freeThe Flutter framework offers a UI toolkit for developers to build web, mobile and desktop apps from a single code base. Flutter started with the goal of making iOS-Android cross-platform development easier, but the focus has expanded beyond mobile. This open-source project, developed at Google, now powers the Google Home Hub. Last month, the first technical preview of Flutter for web development arrived for early adopters to try it out, particularly for interactive content. File under easy listening (and speaking)“Translatotron” may sound like a friendly traveling robot, but it’s actually an experimental speech-to-speech translation system that works differently from the systems developed over the past few decades. These systems usually translate the source speech to text, then translate it into the target language, then generate speech from that text into the target language. This works well, but Translatotron doesn’t divide the task into separate stages, which means it avoids compounding errors between recognition and translation, and does faster inference. It works by inputting and outputting spectrograms, using a trained neural vocoder for waveform translation and speaker encoder to maintain voice character. Check out the full post for details and audio clips demonstrating the system.See how huge image datasets come togetherLast month, Google AI engineers released Google-Landmarks-v2, a new landmark recognition dataset that follows up on last year’s Google-Landmarks. That one was the largest available at the time, but this one is even bigger, with more than 5 million images, twice that of the first version. To really advance research on instance-level recognition (recognizing specific instances of an object such as a landmark) and image retrieval, it’s important to have ever-larger datasets to add variety and train better systems. So this new dataset brings more diversity of images and greater challenges for technologists and tools. Creating the dataset involved crowdsourcing the labeling of landmarks within the photographer community, and using public institution photos. Make sure to check out the accompanying Kaggle competitions, one on image retrieval and one on image recognition.Make a do-it-yourself cloud at homeForget building a treehouse or hanging a flat-screen TV: Here’s a tutorial for you to build a smart home cloud to connect all your devices securely. The device cloud described here uses GCP components, including Firebase, to make a serverless setup to see when devices are offline, provision them to individual users, and more. You’ll get a look along the way into Cloud IoT Core for linking devices, plus Cloud Functions to move data between Cloud IoT Core and Firebase.That’s a wrap for this edition. Let us know what you’re reading!
Quelle: Google Cloud Platform

18. Juni 2019

da Agency

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.

RDP and SSH are both a fundamental approach through which customers connect to their Azure workloads. 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 today, I'm excited to announce the preview of Azure Bastion.

Azure Bastion is a new managed PaaS service that provides seamless RDP and SSH connectivity to your virtual machines over the Secure Sockets Layer (SSL). This is completed without any exposure of the public IPs on your virtual machines. Azure Bastion provisions directly in your Azure Virtual Network, providing bastion host or jump server as-a-service and integrated connectivity to all virtual machines in your virtual networking using RDP/SSH directly from and through your browser and the Azure portal experience. This can be executed with just two clicks and without the need to worry about managing network security policies.

Leading up to the preview, we have worked with hundreds of customers across a wide area of industries. The interest to join the preview has been immense, and similar to other unique Azure services such as Azure Firewall, the feedback has been very consistent: We need an easy and integrated way to deploy, run, and scale jump-servers or bastion hosts within our Azure infrastructure.

For example, what we heard directly from a cloud foundation team manager for a German premium car manufacturer is that they had concerns about exposing cloud virtual machines with RDP/SSH ports directly to the Internet due to the potential of experiencing a number of security and connectivity issues. During the preview of Azure Bastion, they were able to use RDP/SSH over SSL to our virtual machines which allowed them to traverse corporate firewalls effortlessly and at the same time, restrict Azure Virtual Machines to only private IPs.

Deploying a stand-alone dedicated jump-server often entails manually deploying and managing specialized IaaS based solutions and workloads, such as relational database service (RDS) gateway, the configuration and managing of authentication, security policies and access control lists (ACLs), as well as managing availability, redundancy, and scalability of the solution. Additionally, monitoring and auditing along with the ongoing requirement to remain compliant with corporate policies can quickly make the setup and management of jump servers an involving, costly, and less desirable task.

Azure Bastion is deployed in your virtual network providing RDP/SSH access for all authorized virtual machines connected to the virtual network.

Key features available with the preview include:

RDP and SSH from the Azure portal: Initiate RDP and SSH sessions directly in the Azure portal with a single-click seamless experience.
Remote session over SSL and firewall traversal for RDP/SSH: HTML5 based web clients are automatically streamed to your local device providing the RDP/SSH session over SSL on port 443. This allows easy and securely traversal of corporate firewalls.
No public IP required on Azure Virtual Machines: Azure Bastion opens the RDP/SSH connection to your Azure virtual machine using a private IP, limiting exposure of your infrastructure to the public Internet.
Simplified secure rules management: Simple one-time configuration of Network Security Groups (NSGs) to allow RDP/SSH from only Azure Bastion.
Increased protection against port scanning: The limited exposure of virtual machines to the public Internet will help protect against threats, such as external port scanning.
Hardening in one place to protect against zero-day exploits: Azure Bastion is a managed service maintained by Microsoft. It’s continuously hardened by automatically patching and keeping up to date against known vulnerabilities.

Azure Bastion–The road ahead

Like with all other Azure networking services, we look forward to building out Azure Bastion and adding more great capabilities as we march towards general availability.

The future brings Azure Active Directory integration, adding seamless single-sign-on capabilities using Azure Active Directory identities and Azure Multi-Factor Authentication, and effectively extending two-factor authentication to your RDP/SSH connections. We are also looking to add support for native RDP/SSH clients so that you can use your favorite client applications to securely connect to your Azure Virtual Machines using Azure Bastion, while at the same time enhance the auditing experience for RDP sessions with full session video recording.

We encourage you all to try out the Azure Bastion and look forward to hearing and incorporating your feedback.
Quelle: Azure