Wer schon immer zwei Tage lang wie in einem Star-Wars-Abenteuer leben wollte, bekommt ab dem Frühjahr 2022 die Chance dazu – das nötige Kleingeld vorausgesetzt. (Star Wars, Disney)
Quelle: Golem
Das Model 2 von Tesla soll angeblich noch in diesem Jahr in die Testproduktion gehen und wie bei Tesla üblich sehr früh vorgestellt werden. (Tesla, Technologie)
Quelle: Golem
Quelle: <a href="Elizabeth Holmes Is About To Go On Trial, And Theranos Patients Will Be Allowed To Testify Against Her“>BuzzFeed
See how Podman, Red Hat Enterprise Linux, and Amazon solutions can work together to host your photo gallery—and make it highly available.
Quelle: CloudForms
Completes rigorous testing for deployments in U.S. Federal Government CAMPBELL, Calif., August 4, 2021 – Mirantis, the open cloud company, has received a new certificate of FIPS 140-2 validation covering the encryption modules for Mirantis Container Runtime, Mirantis Kubernetes Engine and the k0s Kubernetes distribution, assuring customers high levels of security for cloud native deployments. … Continued
Quelle: Mirantis
One great thing about cloud computing is that it provides you an opportunity to easily run other operating systems — for example, I have a Mac laptop but often need Ubuntu — but unfortunately, getting access to the GUI for things like the browser or other non-command-line tasks often isn’t available out of the box. … Continued
Quelle: Mirantis
Digitec Galaxus AG is the biggest online retailer in Switzerland, operating two online stores: Digitec, Switzerland’s online market leader for consumer electronics and media products, and Galaxus, the largest Swiss online shop with a steadily growing range of consistently low-priced products for almost all daily needs. Known for its efficient, personalized shopping experiences, it’s clear that Digitec Galaxus understands what it takes to deliver a platform that is interesting and relevant to customers every time they shop. The problem: Personalizing decisions for every situationDigitec Galaxus already had established an engine to help them personalize experiences for shoppers when they reached out to Google Cloud. They had multiple recommendation systems in place and were also extensive early adopters of Recommendations AI, which already enabled them to offer personalized content in places like their homepages, product detail pages, and their newsletter. But those same systems sometimes made it difficult to understand how best to combine and optimize to create the most personalized experiences for their shoppers. Their requirements were threefold:Personalization: They have over 12 recommenders they can display on the newsletter, however they would like to contextualize this and choose different recommenders (which in turn select the items) for different users. Furthermore they would like to exploit existing trends as well as experiment with new ones.Latency: They would like to ensure that the solution is architected so that the ranked list of recommenders can be retrieved with sub 50 ms latency.End-to-end easy to maintain & generalizable/modular architecture: Digitec Galaxus wanted the solution to be architected using an easy to maintain, open source stack, complete with all MLops capabilities required to train and use contextual bandits models. It was also important to them that it is built in a modular fashion such that it can be adapted easily to other use cases which have in mind such as recommendations on the homepage, Smartags and more . To improve, they asked us to help them implement a machine learning (ML) contextual bandit based recommender system on Google Cloud taking all the above factors into consideration to take their personalization to the next level. Contextual bandits algorithms are a simplified form of reinforcement learning and help aid real-world decision making by factoring in additional information about the visitor (context) to help learn what is most engaging for each individual. They also excel at exploiting trends which work well, as well as exploring new untested trends which can yield potentially even better results. For instance, imagine that you are personalizing a homepage image where you could show a comfy living room couch or pet supplies. Without a contextual bandit algorithm, one of these images would be shown to someone at random without considering information you may have observed about them during previous visits. Contextual bandits enable businesses to consider outside context, such as previously visited pages or other purchases, and then observe the final outcome (a click on the image) to help determine what works best. Creating a personalization system with contextual banditsWhile Digitec Galaxus heavily personalizes their website homepages, they are very very sensitive and also require more cross-team collaboration to update and make changes. Together with the Digitec Galaxus team, we decided to narrow the scope and focus on building a contextual bandit personalization system for the newsletter first. The Digitec Galaxus team has complete control over newsletter decisions and testing various ML experiments on a newsletter would have less chance of adverse revenue impact than a website homepage. The main goal was to architect a system that could be easily ported over to the homepage and other services offered by Digitec Galaxus with minimal adaptations. It would also need to satisfy the functional and non-functional requirements of the homepage as well as other internal use cases.Below is a diagram of how the newsletter’s personalization recommendation system works:Click to enlargeThe system is given some context features about the newsletter subscriber such as their purchase history and demographics. Features are sometimes referred to as variables or attributes, and can vary widely depending on what data is being analyzed. The contextual bandit model trains recommendations using those context features and 12 available recommenders (potential actions). The model then calculates which action is most likely to enhance the chance of reward (a user clicking in the newsletter) and also minimize the problem (an unsubscribe). It also ensures to exploit well known trends and explore new trends with potentially higher user engagement.Calculating whether a click was a newsletter or an unsubscribe enabled the system to optimize for increasing clicks and avoid showing non-relevant content to the user (click-bait). This enabled Digitec Galaxus to exploit popular trends while also exploring potentially better-performing trends. How Google Cloud helpsThe newsletter context-driven personalization system was built on Google Cloud architecture using the ML recommendation training and prediction solutions available within our ecosystem. Below is a diagram of the high-level architecture used:Click to enlargeThe architecture covers three phases of generating context-driven ML predictions, including: ML Development: Designing and building the ML models and pipeline Vertex Notebooks are used as data science environments for experimentation and prototyping. Notebooks are also used to implement model training, scoring components, and pipelines. The source code is version controlled in Github. A continuous integration (CI) pipeline is set up to automatically run unit tests, build pipeline components, and store the container images to Cloud Container Registry. ML Training: Large-scale training and storing of ML models The training pipeline is executed on Vertex Pipelines. In essence, the pipeline trains the model using new training data extracted from BigQuery and produces a trained, validated contextual bandit model stored in the model registry. In our system, the model registry is a curated Cloud Storage. The training pipeline uses Dataflow for large scale data extraction, validation, processing, and model evaluation, and Vertex Training for large-scale distributed training of the model. AI Platform Pipelines also stores artifacts, the output of training models, produced by the various pipeline steps to Cloud Storage. Information about these artifacts are then stored in an ML metadata database in Cloud SQL. To learn more about how to build a Continuous Training Pipeline, read the documentation guide.ML Serving: Deploying new algorithms and experiments in production The training pipeline uses batch prediction to generate many predictions at once using AI Platform Pipelines, allowing Digitec Galaxus to score large data sets. Once the predictions are produced, they are stored inCloud Datastore for consumption. The pipeline uses the most recent contextual bandit model in the model registry to evaluate the inference dataset in BigQuery and give a ranked list of the best newsletters for each user, and persist it in Datastore. A Cloud Function is provided as a REST/HTTP endpoint to retrieve the precomputed predictions from Datastore.All components of the code and architecture are modular and easy to use, which means they can be adapted and tweaked to several other use cases within the company as well.Better newsletter predictions for millionsThe newsletter prediction system was first deployed in production in February, and Digitec Galaxus has been using it to personalize millions of newsletters a week for subscribers. The results have been impressive, 50% higher than initial baseline. However, the collaboration is still ongoing to improve the results even more. “Working at this level in direct exchange with Google’s machine learning experts is a unique opportunity for us. The use of contextual bandits in the targeting of our recommendations enables us to pursue completely new approaches in personalization by also personalizing the delivery of the respective recommender to the user. We have already achieved good results in our newsletter in initial experiments and are now working on extending the approach to the entire newsletter by including more contextual data about the bandits arms. Furthermore, as a next step, we intend to apply the system to our online store as well, in order to provide our users with an even more personalized experience. To build this scalable solution, we are using Google’s open source tools such as TFX and TF Agents, as well as Google Cloud Services such as Compute Engine, Cloud Machine Learning Engine, Kubernetes Engine and Cloud Dataflow.”—Christian Sager, Product Owner, Personalization (Digitec Galaxus)Since the existing architecture and system is also dynamic, it will automatically adapt to new behaviours, trends, and users. As a result, Digitec Galaxus plans to re-use the same components and extend the existing system to help them improve the personalization of their homepage and other current use cases they have within the company. Beyond clicks and user engagement, the system’s flexibility also allows for future optimization of other criteria. It’s a very exciting time and we can’t wait to see what they build next!Related ArticleIKEA Retail (Ingka Group) increases Global Average Order Value for eCommerce by 2% with Recommendations AIIKEA uses Recommendations AI to provide customers with more relevant product information.Read Article
Quelle: Google Cloud Platform
At Google Cloud, we believe in making it simple and secure to consume services whether they’re from Google, a third party or customer-owned. With Private Service Connect, we have adopted a service-centric approach to our network that abstracts the underlying networking infrastructure. And today, we are announcing Private Service Connect is generally available in all Google Cloud regions.Private Service Connect allows you to create private and secure connections from your cloud networks to services like Cloud Storage or Cloud Bigtable and third-party services like Elastic, MongoDB or Snowflake. It creates service endpoints in your VPCs that provide private connectivity and policy enforcement, allowing you to easily connect to services across different networks and organizations.Customers told us they want to consume services faster while making sure that the connectivity is private and secure. In the past, achieving this was a challenge: networking teams had to negotiate IP address blocks, mutually agree on policies and coordinate as applications evolved to newer versions. With Private Service Connect, you can delegate the consumption and delivery of services to different teams without having to coordinate between teams.How it worksPrivate Service Connect makes it easy to consume services by leveraging service endpoints that are locally managed. The services can be in different projects or managed by different organizations. Access to the service is controlled by strict governance and IAM policies. Application teams and developers can focus on delivering their services easily by exposing their ‘service attachment’. No more worrying about networking constructs—Private Service Connect takes care of connecting to the service on the Google backbone for them. Benefits to our partnersBeing able to consume services from a variety of software vendors and service providers makes it possible for enterprises to innovate faster. For that, developers need to be able to compose services from third-party vendors, Google managed services, as well as their own services. To help, third-party partners can use Private Service Connect to deliver multi-tenant services securely and at massive scale, and make the connectivity to their services appear as if they are running on the enterprises’ network. Private Service Connect will also integrate with Service Directory to register many producer services, making service consumption even simpler. “In today’s environment, where seamless access to real-time market information and the ability to handle increasingly vast volumes of data is essential, our clients are demanding native connectivity in the cloud. Google’s Private Service Connect offers the performance and reliability required by the types of mission critical apps that rely on Bloomberg’s tick for tick market data feed, B-PIPE.” —Cory Albert, Global Head of Cloud Strategy, Enterprise Data at Bloomberg “One of the key goals for Elastic on Google Cloud is to monitor and protect our customers’ data. Google Cloud’s Private Service Connect with Elastic Cloud furthers our commitment to our customers that together we make it quick, easy and secure to gain insights and intelligence from their data.” —Uri Cohen, Product Lead for Elastic Cloud“MongoDB’s partnership with Google is an integral part of our strategy to support modern apps and mission-critical databases and to become a cloud data company. Private Service Connect allows our customers to connect to MongoDB Atlas on Google Cloud seamlessly and securely and we’re excited for customers to have this additional and important capability.”—Andrew Davidson, VP of Cloud Product, MongoDBCheck out the Google Cloud Console to try it today.Related ArticleRegistration is open for Google Cloud Next: October 12–14Register now for Google Cloud Next on October 12–14, 2021Read Article
Quelle: Google Cloud Platform
Visualizing trends in your logs is critical when troubleshooting an issue with your application. Using the histogram in Logs Explorer, you can quickly visualize log volumes over time to help spot anomalies, detect when errors started and see a breakdown of log volumes. But static visualizations are not as helpful as having more options for customization during your investigations. That’s why we’re excited to announce that we recently added three new query controls along with separate colors for log severity to the histogram. These new features make it even easier to refine and analyze your logs by time range. The new histogram controls help find logs before or after the current period, jump to a specific time range represented in a histogram bar and zoom in/out of the current time window in the histogram.Histogram colorsThe histogram now makes it easier to view the breakdown of logs by severity with the introduction of color coding. For example, the severity colors make it easy to spot an increasing number of errors even when the volume of requests is relatively constant. Looking at the histogram below, the red vs blue shading makes it clear that there has been an increase in overall log volume and provides a visual breakdown of errors within that log volume.A screenshot of the new color coding for logs in the histogramPan left/right to scroll through timeSometimes in your troubleshooting journey, you may want to look at the logs directly before or after the current set of logs. Perhaps there was an unexpected spike in errors at the beginning of the time range and you need to see the logs in the time period directly preceding the current time range. Pressing the left arrow on the left side of the histogram shifts the time range earlier while the arrow on the right side of the histogram shifts the time range ahead. Either arrow will refine the time range in the query and rerun the query to return the logs in the new time range.An example of the right and left scrolling to adjust which time frame you are viewing in the histogram Zooming in or out Zooming in or out from a given time range may be useful to visualize fine-grained details or a broader trend Clicking the zoom in or out icons in the upper right corner of the histogram refines the time range in the query and then reruns the query, returning the logs in the newly defined time range.A view of the zoom in and zoom out feature to adjust the time scale of the histogramScrolling to time If you see a large spike in logs volume in the histogram, it’s useful to quickly review the logs generated during that spike. Clicking on the histogram bar that contains the spike now scrolls you to the logs generated during that time period.Click on the histogram bar to filter the logs viewWhere to find the histogram The histogram is a panel in Logs Explorer that can be displayed or hidden using the controls in the Page Layout menu. When you no longer want to display the histogram, click the “X” button in the upper right corner to quickly close it. To open it again, use the same Page Layout menu to enable the histogram display.A view of where to find the histogram in the Page Layout menu in Logs ExplorerGet started with the histogramThese improvements move the histogram from a utility for visualization to an integral part of the troubleshooting journey. We are continuously working to launch new features that make Cloud Logging the best place to troubleshoot your Google Cloud logs. If you are not already a Cloud Logging user, review this getting started documentation or watch a quick video ontroubleshooting services on Google Kubernetes Engine (GKE)to learn more. If you have specific questions or feedback, please join the discussion on our Google Cloud Community, Cloud Operations page.Related ArticleRead Article
Quelle: Google Cloud Platform
For the fourth consecutive year, Gartner has positioned Google as a Leader in the 2021 Gartner Magic Quadrant for Cloud Infrastructure and Platform Services (formerly titled as Magic Quadrant for Cloud Infrastructure as a Service upto 2014 Infrastructure as a Service, or (IaaS).With our customers and communities adjusting to new ways of working and doing business, Google Cloud has remained focused on building services and platforms that help you be more resilient and derive even more value from your cloud infrastructure. We believe Gartner’s analysis and recognition gives our customers the confidence needed to choose Google as the platform for customer-centric innovation. Here are just a few recent examples.Ready for the most demanding, mission-critical workloadsOur enterprise-ready cloud provides you the uptime, performance, and scale to run even your most demanding workloads. Examples of recent launches: The largest single-node GPU-enabled VM in the industry with up to 16 NVIDIA A100 instances so that our customers can run their ML workloads The only cloud to support scale-out out 96TB SAP HANA so that customers can confidently bring their most critical workloads to GCPStrategic partnerships with leading partners like SAP Several regions and an expanded global network footprint including new subsea cables, Firmina, Dunant, Blue and RamanHigh bandwidth 50/75/100Gbps networking for VMsPersistent Disk Extreme (block storage) with 120K IOPS Filestore High Scale scale-out NFS for HPCSaves you moneySave money with a transparent and innovative approach to pricing and intelligent recommendations. In the past year, we’ve launched several innovations to help you save costs:Tau VMs, which offer the best price-performance among leading clouds for scale-out workloads Machine-learning-driven predictive auto-scaling for VMs and GKE Autopilot, enabling infrastructure to scale up and down as needed with minimal waste Standard network tier which routes traffic over the internet for cost optimization OpenWe have a long history of leadership in open technologies—from projects like Kubernetes, the industry standard in container orchestration and interoperability, to TensorFlow, a platform to help anyone develop and train machine learning models. Here are a few recent improvements we’ve made to ensure your cloud is an open cloud: Extended Anthos to bare metal and Microsoft Azure to support customers who want a multi-cloud and hybrid cloud posture. Announced a new network dataplane for Google Kubernetes Engine (GKE) and Anthos that supports eBPF, an open-source Linux kernel technology optimized for Kubernetes. Google Kubernetes Engine (based on the Kubernetes standard) received the top overall score based on 2021 Gartner Solution Scorecard for Google Kubernetes Engine.SecureGoogle Cloud’s trusted infrastructure uses layers of security to protect your data with advanced technologies and operations, keeping your organization secure and compliant. For example, we offer:Confidential VMs and Confidential GKE with in-memory encryption and encryption keys controlled by you, with a single checkboxEnhanced security for Cloud RunStrong support against DDoS attacks. In 2017, our infrastructure absorbed the largest-known DDoS attack at 2.5Tbps with no impact to customers. SustainableGoogle Cloud helps customers transform their business sustainably. We operate the cleanest cloud in the industry to make sure your digital footprint doesn’t leave a carbon one. Here are a few proof points:Google has been carbon neutral since 2007, and for the past four years has matched 100% of the electricity we consume globally with wind and solar purchases. Everything you run on Google Cloud is net carbon neutral. We continue to innovate towards greater energy efficiency in our data centers, and compared with five years ago, now deliver around seven times as much computing power with the same amount of electrical power.Recently we announced new features to help customers reduce the carbon footprint of their applications and infrastructure, including a region picker to help with architecture decisions, and low carbon indicators in the Google Cloud Console. Supporting our customersMost importantly, our field organizations and partner organizations work with a singular focus to ensure customer success. This has made Google Cloud the fastest growing hyperscaler, with a rapidly expanding customer base across all geos and industries.Since launching Customer Care last year, we consolidated and simplified the post-sales engagement with customers, increased the support channels, created an API to allow programmatic case creation, and combined product specific support into a single package for all of Google Cloud. Enterprises with Customer Care continue to report high levels of satisfaction with their focused technical account managers (TAMs), helping them get the most business value out of Google Cloud.We are committed to sustaining and accelerating the pace of customer-centric innovation. You can download a complimentary copy of the 2021 Magic Quadrant for Cloud Infrastructure and Platform Services on our website. Join us to learn much more about Google Cloud at the upcoming Google Cloud Next ‘21digital conference. Gartner, Magic Quadrant for Cloud Infrastructure and Platform Services, Raj Bala | Bob Gill | Dennis Smith | Kevin Ji | David Wright, 27 July 2021Gartner, Solution Scorecard for Google Kubernetes Engine, Tony Iams | Traverse Clayton | Megan Bain, 12 April 2021Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.Related ArticleRegistration is open for Google Cloud Next: October 12–14Register now for Google Cloud Next on October 12–14, 2021Read Article
Quelle: Google Cloud Platform
