Mit dem Snapdragon 765 hat Qualcomm den ersten eigenen Chip mit integriertem 5G-Baseband, der Snapdragon 765G richtet sich mit schnellerer Grafikeinheit explizit an Spieler. Das Topmodell, der Snapdragon 865, hingegen ist ein Application Processor, der nicht mal LTE beherrscht. (Snapdragon, Smartphone)
Quelle: Golem
Many customers have questions when it comes to managing cloud operations. How can I implement real-time cloud governance at scale? What’s the best way to monitor my cloud workloads? How can I get help when I need it?
Azure offers a great deal of guidance when it comes to optimizing your cloud operations. At the organizational level, the Microsoft Cloud Adoption Framework for Azure can help you design and implement your approach to management and governance in the cloud. At the cloud resource level, Azure Advisor provides personalized recommendations to help you optimize your Azure workloads for a variety of objectives—including cost savings, security, performance, and availability—based on your usage and configurations.
Recently, Advisor introduced a new recommendation category—operational excellence—to help you follow best practices for process and workflow efficiency, resource manageability, and deployment.
Introducing a new Azure Advisor recommendation category: operational excellence
Azure Advisor now offers a new category of recommendations—operational excellence—to help you optimize your cloud process and workflow efficiency, resource manageability, and deployment practices. You can get these recommendations from Advisor in the operational excellence tab of the Advisor dashboard. They’re also available via Advisor’s CLI and API.
The operational excellence category is launching with nine recommendations, and more on the way. Examples include creating Azure Service Health alerts to be notified when Azure service issues affect you; repairing invalid log alert rules; and following best practices using Azure policy, such as tag management, geo-compliance requirements, and specifying permitted virtual machine (VM) SKUs for deployment. Together, these recommendations will help you optimize your cloud operations practices.
New operational excellence recommendations
Here’s a quick round-up of the new operational excellence recommendations in Advisor at launch:
Create Azure Service Health alerts to be notified when Azure service issues affect you.
Design your storage accounts to prevent hitting the maximum subscription limit.
Ensure you have access to Azure cloud experts when you need it.
Repair invalid log alert rules.
Follow best practices using Azure Policy, including tag management, geo-compliance requirements, and VM audits for managed disks.
For more detailed information on Advisor’s operational excellence recommendations, refer to our documentation. Be sure to check back regularly, as we’re constantly adding new recommendations.
Review your operational excellence recommendations today
Visit Advisor in the Azure portal here to start optimizing your cloud workloads for operational excellence. For more in-depth guidance, visit our documentation. Let us know if you have a suggestion for Advisor by submitting an idea here.
Quelle: Azure
You can customize the OpenShift Container Platform web console to set a custom logo and product name. This is especially helpful if you need to tailor the web console to meet specific corporate or government requirements.
Add a Custom Logo and Product Name
Prerequisites
Create a file of the logo that you want to use. The logo can be a file in any common image format (e.g. GIF, JPG, PNG, or SVG) and is constrained to a max-height of 60px.
Procedure
1: Import your logo file into a ConfigMap in the openshift-config namespace:
$ oc create configmap console-custom-logo –from-file~/path/to/console-custom-logo.png -n openshift-config
2: Edit the web console’s Operator configuration to include customLogoFile and customProductName:
$ oc apply -f
apiVersion: operator.openshift.io/v1
kind: Console
metadata:
name: cluster
spec:
customization:
customProductName: MyProduct
customLogoFile:
name: console-custom-logo
key: console-custom-logo.png
Once the Operator configuration is updated, it will sync the custom logo ConfigMap into the console namespace, mount it to the console pod, and redeploy.
3: Check for success. If there are any issues, the console cluster operator will report Degraded, and the console Operator configuration will also report CustomLogoDegraded, but with reasons like KeyOrFilenameInvalid or NoImageProvided.
To check the clusteroperator, run:
$ oc get clusteroperator console -o yaml
To check the console Operator configuration, run:
$ oc get console.operator.openshift.io -o yaml
The post OpenShift 4 Pro Tip: Custom Branding appeared first on Red Hat OpenShift Blog.
Quelle: OpenShift
Building dependable, flexible data integration to gather the data your business needs, and preparing it for data analytics, is an essential step toward successful big data analytics. But traditional data processing and DIY ETL processes are complex and time-consuming, slowing down data analysis. At Google Cloud, our aim is to radically simplify data integration and ingestion processes to accelerate time to insights. Code-free development of ETL and ELT data pipelines is here. We’re announcing the general availability of Cloud Data Fusion, a managed, cloud-native data ingestion and integration service that can bring the capabilities of a seasoned data engineer to any team—whether they know a little code or none at all.Data Fusion equips developers, data engineers, and business analysts to easily build and manage ETL and ELT pipelines to cleanse, transform and blend data from a broad range of sources. You can skip the expertise bottlenecks and focus instead on learning from your data. Built on the open source project CDAP, Data Fusion’s open core ensures portability for users across hybrid and multi-cloud environments. CDAP’s broad integration with on-premises and public cloud platforms helps Data Fusion users easily access Google Cloud’s big data and analytics tools, like BigQuery.Data Fusion lets Vodafone deliver BI modernization in weeks, not quartersVodafone is rethinking data and analytics as they move from complex BI to actionable insights. With Cloud Data Fusion, the company is successfully modernizing BI stack operations across global markets.“Modernizing the BI stack for 26 operating countries is complex and challenging,” says Osman Peermamode, director of business intelligence and analytics at Vodafone Group. “Cloud Data Fusion is one of the fundamental and critical building blocks to BI modernization. With Data Fusion, we are able to quickly aggregate data from various sources, cleanse and blend without code, and standardize pipelines for faster delivery of projects. It not only improves productivity but has also provided agility to transform multiple markets quickly. Additionally, we are now able to access data loads and reports faster; 25 minutes runtime today vs. 36 hours previously. Finally, Data Fusion lineage capability has provided much-needed insights into the quality of KPIs. We are very excited to partner with Google Cloud and the Data Fusion team to make our BI transformation a success.”Google Cloud customers use Data Fusion to build modern data warehouses and support their BI transformation in cloudWe have been listening to Data Fusion beta users, and now, Data Fusion is generally available, along with the features that our users asked for. Here are some of the new capabilities we are launching in Data Fusion:Secure access to on-premises data with private IPEncryption of data at-rest with Customer Managed Encryption Keys (CMEK) VPC Service Controls for preventing data exfiltrationField-level data lineage in AlphaExpanded connector ecosystemGetting to know Data FusionData Fusion can make it much easier to build pipelines and bring all your data together. Here’s more detail about the recently launched features. Securely access on-premises data with Private IPSecuring the movement of data should be easy. With private service access in Data Fusion, you can lock down an instance to run entirely on private IP-only compute resources not accessible through the public internet. Instances can now connect to on-premises resources, such as RDBMS, securely over a private network. This means you no longer have to make prohibitive networking changes to access your data from Data Fusion.Encryption of data at rest with Customer Managed Encryption Keys Encryption of data at rest is foundational to any data protection strategy. Google Cloud Platform (GCP) encrypts data at rest using Google’s default encryption keys. In addition to providing encryption by default, Data Fusion now supports Customer Managed Encryption Keys (CMEK) for even greater levels of control across all user data in supported storage systems. You can read CMEK-encrypted data as a source, and will also be able to specify CMEK keys for encrypting all data written by Data Fusion to supported services on GCP. VPC Service Controls for preventing data exfiltrationThe requirement for the protection of sensitive data is higher than ever. VPC Service Controls allows GCP users to define a security perimeter around platform resources in order to protect private data and mitigate exfiltration risks. With this in mind, we’re happy to announce you can now add Data Fusion instances to your service perimeter and run pipelines in a VPC Service Controls environment. Field-level data lineage, now in AlphaField-level lineage allows enterprises to simplify critical tasks, such as root cause analysis of data errors, analyze the impact of changes, and seamlessly govern their data. It also serves as a key enabler for compliance and regulatory reporting by allowing you to trace data as it flows through, at a granular level, including the transformations that were performed on individual fields.Expanded connector ecosystemThis Data Fusion release also includes new connectors that can help you integrate your data from a variety of relational databases (SAP Hana, Teradata), NoSQL stores (MongoDB) and SaaS applications (Salesforce, Google Analytics 360, etc).No matter where you stand, you’re now ready for data analytics on the cloud! What are you waiting for? Check out the Data Fusion Quickstart Guide and build your first pipeline today.
Quelle: Google Cloud Platform
We’re pleased to announce that Gartner has named Google Cloud a Leader in its 2019 Magic Quadrant report for Operational Database Management Systems (OPDBMS). This news reflects what we hear from our customers: that Google Cloud databases are flexible, open, and easy to use. These include our fully compatible managed services for popular database engines like MySQL, PostgreSQL, SQL Server and Redis, and scalable cloud-native relational and non-relational databases like Cloud Spanner, Cloud Bigtable, and Cloud Firestore, plus fully managed partner services like MongoDB Atlas, Elastic, and Redis Enterprise. You can also run proprietary database workloads on Google Compute Engine or via our Bare Metal Solution.Enterprises databases in productionWe’ve heard great stories from our customers about their use of Google Cloud databases to run their businesses with ease and flexibility. Our database products meet varying needs for scalability and power.Gaming company Bandai Namco Entertainment needed fast scalability, a global network, and real-time analytics to serve users its Dragon Ball Legends game. They were initially considering sharded MySQL to handle the scale, but opted for Cloud Spanner. Because it’s strongly consistent, fully managed, and scales seamlessly, Cloud Spanner supported the game’s rollout and allowed millions of worldwide players to compete without downtime. And media leader The New York Times found our Cloud Firestore database service as they built a truly real-time collaboration tool that lets multiple writers and editors make changes in docs at the same time, keeping track of what’s newest. Cloud Firestore is designed for just this type of task, since it supports offline and real-time sync. E-commerce brand analytics and protection company 3PM Solutions empowers global brands to manage, protect, and grow revenue by using Google Cloud Platform services such as Cloud Bigtable. Using Google Cloud, they’ve been able to analyze 160 million customer reviews of more than 2 million sellers in less than four hours. Learn more about Google Cloud’s databases, check out customer stories, and read the Gartner report here. Gartner 2019 Magic Quadrant for Operational Database Management Systems – November 25, 2019, Merv Adrian, Donald Feinberg, Henry Cook. Gartner 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, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Quelle: Google Cloud Platform
As demand grows for features like personalization systems, efficient information retrieval, and anomaly detection, the need for a solution to optimize these features has grown as well. Contextual bandit is a machine learning framework designed to tackle these—and other—complex situations.With contextual bandit, a learning algorithm can test out different actions and automatically learn which one has the most rewarding outcome for a given situation. It’s a powerful, generalizable approach for solving key business needs in industries from healthcare to finance, and almost everything in between.While many businesses may want to use bandits, applying it to your data can be challenging, especially without a dedicated ML team. It requires model building, feature engineering, and creating a pipeline to conduct this approach.Using Google Cloud AutoML Tables, however, we were able to create a contextual bandit model pipeline that performs as good or better than other models, without needing a specialist for tuning or feature engineering.A better bandit building solution: AutoML TablesBefore we get too deep into what contextual bandits are and how they work, let’s briefly look at why AutoML Tables is such a powerful tool for training them. Our contextual bandits model pipeline takes in structured data in the form of a simple database table, uses the contextual bandit and meta-learning theories to perform automated machine learning, and creates a model that can be used to suggest optimal future actions related to the problem. In our research paper, “AutoML for Contextual Bandits”—which we presented at the ACM RecSys Conference REVEAL workshop—we illustrated how to set this up using the standard, commercially available Google Cloud product.As we describe in the paper, AutoML Tables enables users with little machine learning expertise to easily train a model using a contextual bandit approach. It does this with:Automated Feature Engineering, which is applied to the raw input dataArchitecture Search to compute the best architecture(s) for our bandits formulation task—e.g. to find the best predictor model for the expected reward of each episodeHyper-parameter Tuning through searchModel Selection where models that have achieved promising results are passed onto the next stageModel Tuning and EnsemblingThis solution could be a game-changer for businesses that want to perform bandit machine learning but don’t have the resources to implement it from scratch. Bandits, explainedNow that we’ve seen how AutoML Tables handles bandits, we can learn more about what, exactly, they are. As with many topics, bandits are best illustrated with the help of an example. Let’s say you are an online retailer that wants to show personalized product suggestions on your homepage.You can only show a limited number of products to a specific customer, and you don’t know which ones will have the best reward. In this case, let’s make the reward $0 if the customer doesn’t buy the product, and the item price if they do.To try to maximize your reward, you could utilize a multi-armed bandit (MAB) algorithm, where each product is a bandit—a choice available for the algorithm to try. As we can see below, the multi-armed bandit agent must choose to show the user item 1 or item 2 during each play. Each play is independent of the other—sometimes the user will buy item 2 for $22, sometimes the user will buy item 2 twice earning a reward of $44.The multi-armed bandit approach balances exploration and exploitation of bandits.To continue our example, you probably want to show a camera enthusiast products related to cameras (exploitation), but you also want to see what other products they may be interested in, like gaming gadgets or wearables (exploration). A good practice is to exploit more at the beginning, when the agent’s information about the environment is less accurate, and gradually adapt this policy as more knowledge is gained.Now let’s say we have a customer that’s a professional interior designer and an avid knitting hobbyist. They may be ordering wallpaper and mirrors during working hours and browsing different yarns when they’re home. Depending on what time of day they access our website, we may want to show them different products.The contextual bandit algorithm is an extension of the multi-armed bandit approach where we factor in the customer’s environment, or context, when choosing a bandit. The context affects how a reward is associated with each bandit, so as contexts change, the model should learn to adapt its bandit choice, as shown below.Not only do you want your contextual bandit approach to find the maximum reward, you also want to reduce the reward loss when you’re exploring different bandits. When judging the performance of a model, the metric that measures reward loss is regret—the difference between the cumulative reward from the optimal policy and the model’s cumulative sum of rewards over time. The lower the regret, the better the model.How contextual bandits on AutoML Tables measures upIn “AutoML for Contextual Bandits” we used different data sets to compare our bandit model powered by AutoML Tables to previous work. Namely, we compared our model to the online cover algorithm implementation for Contextual Bandit in the Vowpal Wabbit library, which is considered one of the most sophisticated options available for contextual bandit learning.Using synthetic data we generated, we found that our AutoML Tables model reduced the regret metric as the number of data blocks increased, and outperformed the Vowpal Wabbit offering.We also compared our model’s performance with other models on some other well-known datasets that the contextual bandit approach has been tried on. These datasets have been used in other popular work in the field, and aim to test contextual bandit models on applications as diverse as chess and telescope data. We consistently found that our AutoML model performed well against other approaches, and was exceptionally better than the Vowpal Wabbit solution on some datasets.Contextual bandits is an exciting method for solving the complex problems businesses face today, and AutoML Tables makes it accessible for a wide range of organizations—and performs extremely well, to boot. To learn more about our solution, check out “AutoML for Contextual Bandits.” Then, if you have more direct questions or just want more information, reach out to us at google-cloud-bandits@google.com.The Google Cloud Bandits Solutions Team contributed to this report: Joe Cheuk, Cloud Application Engineer; Praneet Dutta, Cloud Machine Learning Engineer; Jonathan S Kim, Customer Engineer; Massimo Mascaro, Technical Director, Office of the CTO, Applied AI
Quelle: Google Cloud Platform
This post was co-authored by Omar Aftab, Partner Director of Program Management, Power Virtual Agents.
Conversational artificial intelligence (AI) is enabling organizations to improve their business in areas like customer service and employee engagement by automating some of the most commonly requested services, which frees up employees to take on more value-adding activities. While the benefits of conversational AI are well established, determining who in an organization should build these solutions is not always clear.
As is true of many applications, conversational AI solutions (or bots) can be built using software-as-a-service (SaaS) or platform-as-a-service (PaaS) offerings. Consequently, organizations are forced to decide between empowering business users who are closest to the business problems or empowering developers with coding experience to have full control over how these solutions are built, without many options to bridge the gap and allow for collaboration between the two. However, with the integration of Bot Framework Skills into Microsoft Power Virtual Agents (a graphical interface offering for business users creating bots, now generally available), Microsoft uniquely empowers both business users and developers to collaborate seamlessly in building conversational AI solutions.
In the bot building journey, bot builders across the organization should not work in siloes. If a business user is building a bot but wants to add a nuanced scenario, they should be able to collaborate with a developer who can customize the bot further. Similarly, developers building a bot can also leverage bots that have been built by business users as a skill.
Microsoft offers an end-to-end, no-cliffs bot building experience with Power Virtual Agents and Bot Framework.
Power Virtual Agents provides a no-code experience for bot development – ideal for business users and domain experts to easily build a bot, without having to worry about the technical aspects of bot development.
Bot Framework is an open-source SDK and tools purpose-built for bot development – ideal for developers who want to build a bot using a code experience and want full control of technical aspects of bot development, including language model ownership, and visual design. Additionally, Azure Bot Service allows developers to host and deploy their bots to popular channels like Teams and other messaging platforms where users will interact with the bot.
Bot Framework Skills offering a no-cliffs bot building experience – no matter your starting point. With Bot Framework Skills, Power Virtual Agents users have a no-cliff bot building experience because they can collaborate with Bot Framework developers to extend their bots with custom capabilities. Equally important, Bot Framework developers can extend their bot as a skill and allow subject matter experts to update bot conversations.
For example, suppose an organization is creating a travel bot using Power Virtual Agents. The business users build out the dialogs with a UI-based experience that allows the bot to handle customers’ intents, such as Check miles and rewards, Check flight status, Update account information, and Book a flight.
However, what if someone in the organization has already built a Book a Flight skill with custom language models using Bot Framework and Language Understanding service as illustrated below?
In this scenario, business experts can collaborate with the developer who has built this flight booking skill by selecting it as an action in Power Virtual Agents.
As conversational AI adoption continues to grow, we believe it is important for organizations to take an interdisciplinary team approach to bot development. For this reason, Microsoft offers an end-to-end, no-cliffs bot building experience that empowers business subject matter experts and developers alike to collaborate.
Get started with Power Virtual Agents.
Get started with Bot Framework.
Learn more about how to extend your Power Virtual Agents bot with Bot Framework Skills.
Quelle: Azure
Rendergrafiken der Devkits gibt es schon länger. Jetzt ist erstmals ein Foto mit Entwicklersystemen der Playstation 5 aufgetaucht. Seit ein paar Tagen ist zudem relativ klar, welcher Hersteller die besonders schnellen SSDs für die Konsole zuliefern dürfte. (Playstation 5, Sony)
Quelle: Golem
Um die Gigabit-Ziele bis 2025 zu erreichen, müsste die Bürokratie verschwinden und es müssten alternative Verlegeverfahren zugelassen werden. Doch selbst dann bleibt es schwierig. (Politik/Recht, Long Term Evolution)
Quelle: Golem
Die US-Raumfahrtbehörde Nasa hat Bilder der Stelle veröffentlicht, wo der indische Lander Vikram auf dem Mond aufgeschlagen ist. Die Mondlandung war im September gescheitert. (Raumfahrt, Wissenschaft)
Quelle: Golem
