Amazon Kendra ist ein intelligenter Suchservice der auf Machine Learning basiert, mit dem Unternehmen ihren Kunden und Mitarbeitern bei Bedarf notwendige Informationen zur Verfügung stellen können. Ab heute können AWS-Kunden den Amazon Kendra Dropbox-Connector verwenden, um Dokumente aus der Dropbox-Datenquelle zu indizieren und zu durchsuchen.
Quelle: aws.amazon.com
Die neue Apple Watch Ultra zeigt beim Scrollen von Bildschirminhalten nach Nutzermeldungen angeblich einen Jelly-Scrolling-Effekt. (Apple Watch, Apple)
Quelle: Golem
Die Karosserie des Elektroautos Citroën Oli besteht aus recyceltem Karton. Das Auto wiegt unter 1.000 kg und soll 400 km weit kommen. (Citroën, Technologie)
Quelle: Golem
Mehrere Monitore an einem Rechner sind für die effektive IT-Arbeit elementar. Für die Bedienung von mehreren Rechnern mit einem Eingabeset hingegen braucht es Hilfsmittel, zum Beispiel Mouse Without Borders. Von Kristof Zerbe (Helferlein, Microsoft)
Quelle: Golem
Nio nimmt seine erste Akku-Wechselstation für seine Elektroautos in Deutschland in Betrieb. Sie steht an der A8. (Elektroauto, Technologie)
Quelle: Golem
Laut Microsoft führen staatliche Hacker derzeit Angriffe auf Linkedin durch. Dabei arbeiten sie mit um Schadfunktionen erweiterter Open-Source-Software. (Microsoft, Internet)
Quelle: Golem
Ever since the block editor was introduced in 2018, we’ve been pushing it to do more — and our latest crop of improvements will help you build posts and pages on your site with confidence, no matter what you publish:
More design tools for dozens of blocks
More control over lists with our improved List Block
Easier block-switching
Let’s take a closer look at each.
More Design Tools for Dozens of Blocks
We’ve added a variety of design tools to dozens of blocks. Depending on the block, you’ll now see typography, color, border, spacing, and layout options.
To play around with these new design tools, select the block you want to work with and use the right-hand sidebar to access these sections.
You’ll see a few controls right away, but with just a couple clicks you can go even deeper. Select the three dots on the upper right of each section to find even more options, or hit the red chain link button to allow control over borders and/or padding for each side or corner individually.
Without a doubt, one of the most fun additions is having more border options on Image Blocks:
More Control Over Lists
Our improved List Block means that lists are easier to manage from the toolbar. Every item and hierarchy in your list can now be manipulated as its own block. Rearranging, grouping, and nesting items has never been easier:
Easier Block-Switching
The block transforms menu offers a quick shortcut to switch to a different block, depending on your content. As the number of blocks has grown and you’ve started using this menu more and more, it’s been made more useful by changing the organization of what’s displayed at the top. For blocks that support paragraph, heading, list, and quote transformations — which are, by far, the most used changes — we’ve now emphasized those blocks:
Keep building with the block editor. We’ll keep improving it.
The block editor is always growing and improving as a result of your feedback, so thank you — we can’t wait to show you what we’re building next. In the meantime, we’ll continue working hard behind the scenes, with more updates to come! Have ideas for how to improve the editor? Let us know in the comments.
Quelle: RedHat Stack
Artificial intelligence (AI) and machine learning (ML) models have become incredibly advanced in the last decade. AI has transformed how we’re served ads, receive recommendations for care at the doctor, and even how we’re helped by customer support teams. With AI playing an increasingly prominent role in the lives of consumers, it’s critical that businesses and their data science teams are equipped with the technologies needed to proactively surface, troubleshoot and resolve ML model performance issues quickly and accurately. Enter Arize AI.Founded in 2020, startup Arize AI mission is to provide organizations with a production-grade infrastructure platform that helps organizations use AI/ML accurately by identifying and fixing any potential ML model issues quickly and seamlessly. Now, Arize is launching its platform on Google Cloud Marketplace, helping scale its product to users around the globe. The startup has also more than doubled its usage of Google Cloud over the last 12 months to meet growing demand for its products among its customers, including leading enterprises across industries including technology and financial services.With Arize’s ML observability platform, machine learning engineers can better understand how their deployed AI is–or isn’t–working. The platform connects offline ML training and validation datasets to customers’ online production data in a central inference store, which in turn enables ML practitioners to pinpoint the source of model performance problems as they surface. Using Arize, engineers can quickly identify issues like data drift and algorithmic bias, address data integrity issues impacting predictions, and improve model interpretability for optimized performance over time.With accelerated AI strategies on the rise at companies across numerous industries, Arize selected Google Cloud as its primary cloud provider so it could scale its cloud-first business with technologies like Google Kubernetes Engine (GKE). Since October 2021, the startup has been significantly expanding its usage of Google Cloud infrastructure and technologies to meet the growing demand for its platform. Today, Arize is furthering its partnership with Google Cloud in a few key ways in order to scale its business more rapidly in the cloud and continue delivering innovative platform advancements to its customers.First, Arize is today making its platform available on Google Cloud Marketplace, expanding its availability to customers globally. Leveraging Google Cloud’s go-to-market expertise, Arize will be able to increase revenues with greater scale and speed. Additionally, this expanded partnership will provide greater migration support to existing Arize customers as they move their on-prem Arize instances onto Google Cloud’s secure, global, and highly performant infrastructure. And for Google Cloud customers looking to get started with Arize, they can simply deploy the platform directly within their cloud environment and begin enhancing their ML observability capabilities.Secondly, Arize will continue to expand its use of GKE, which it uses for its developer hosting production environment and infrastructure support. With GKE, organizations are able to run fully-managed containerized applications with automation, at cloud scale, and on Google Cloud’s flexible, secure infrastructure. The platform elasticity enabled by GKE allows the Arize IT team–a small-but-mighty collective–to easily scale services up or down with demand and provide greater support to Arize developers at scale without getting bogged down with Kubernetes management.Arize also uses GKE as a part of its developer onboarding environment. Within GKE, Arize developers are equipped with a personal name space where they can run their own deployments of Arize using the full Arize stack, all within an isolated test environment. By aligning the company’s software testing and deployment standards with its developer onboarding practice, Arize developers are enabled with the skills and technologies needed to deploy new platform advancements quicker and with fewer bugs–resulting in consistently high developer efficiencies for the startup. Plus, the reliability of GKE abstractions allows Arize to remain nimble as their developer team grows and the business scales its software deployments. Besides leveraging Google’s secure infrastructure and GKE for its hosting production, developer onboarding, and application data management, Arize is also using Google Cloud tools like Cloud Storage to backup its application data, and Google BigQuery for internal analysis and back office services. As AI continues to change the way companies operate and deliver solutions to customers, Google Cloud is proud to support the growth of innovative startups like Arize with infrastructure and cloud technologies so they can empower business and their data science teams to drive accurate AI outcomes for the business and their customers.Click here to learn more about Arize on Google Cloud Marketplace, and why tech companies and startups choose Google Cloud here.Related ArticleWhy automation and scalability are the most important traits of your Kubernetes platformThe recipe for long-term success with Kubernetes: automation that matters and scalability that saves money.Read Article
Quelle: Google Cloud Platform
At Google Cloud, we’re focused on giving customers new ways to strengthen their security posture. Managing identities and authorization is a core security control that underpins interactions inside and collaboration outside the organization. To address fraud, identity theft, and other security challenges associated with the proliferation of online accounts, many organizations have opted to use centralized identity provider (IdP) products that can help secure and manage identities for their users and SaaS applications, and we want to strengthen support for these solutions and the use cases they support.Today we’re pleased to announce Workforce Identity Federation in Preview. This new Google Cloud Identity and Access Management (IAM) feature can rapidly onboard workforce user identities from external IdPs and provide direct secure access to Google Cloud services and resources. Workforce Identity Federation uses a federation approach instead of Directory Synchronization, the method currently used by most organizations for onboarding Google Cloud identities. Workforce Identity Federation provides flexibility to support third-party collaboration use cases and business requirements that can be better addressed by using a localized, customer-managed IdP.Federating existing identities eliminates the need to maintain separate identities across multiple platforms. This means that organizations using Workforce Identity Federation no longer need to synchronize workforce user identities from their existing identity management solutions to Google Cloud. IdPs can include Identity-as-a-Service (IDaaS) and directory products such as those from ForgeRock, Microsoft, Okta, JumpCloud, or Ping Identity.Workforce Identity Federation overview and workflowWorkforce Identity Federation is another example of how we are working to make Google Cloud’s Invisible Security vision a reality, in this case delivering secure access leveraging customers’ current identity and access management solutions without the need for redundant user administration.VMware is one of our customers using Workforce Identity Federation in Preview. Thiru Bhat, director at VMware, explained why he’s excited for the new feature.VMware runs its own IdP and we needed a solution to allow our developers to access their Google Cloud projects while maintaining corporate control over identities and permissions. Syncing of user identities outside of our IdP is not permitted per our InfoSec policies and we deployed Google Cloud’s Workforce Identity Federation to fulfill our identity requirements. Workforce Identity Federation feature meets our needs with a solution that is robust and straightforward to configure.Here’s a closer look at a few use cases and the benefits from the new Workforce Identity Federation.Use case: Employee sign-in and authorization Streamlined authentication experience with fine-grained access controlWorkforce Identity Federation can enable your organization’s users to access Google Cloud through the same login experience they already use for their existing IdP for single sign-on. Workforce Identity Federation also can enable fine-grained access through attribute mapping and attribute conditions. Attributes — which some IdPs call claims — contain additional information about users. Google Cloud can use these attributes to further inform authentication decisions. Attribute mapping lets your administrators map identity attributes that are defined in your IdP to those that Google Cloud can use. Your administrators can configure Google Cloud with attribute conditions to authenticate conditionally — to let only a subset of external identities authenticate to your Google Cloud project based on attributes.For example, your administrators might want to let only those employees who are part of the accounting team sign in. To do this, your administrators can configure an IdP attribute, such as EmployeeJobFamily. Using attribute mapping, they could map this attribute to a similar attribute in Google Cloud, such as employee_job_family. Then, they could configure an attribute condition, assertion.employee_job_family==”accounting”.Use case: Secure access for partners and vendors Restricted and secure access to Google Cloud services from a partner or vendor that has their own IdP and associated privacy and data policiesToday, the modern enterprise depends on partners and vendors more than ever. Partners and vendors can help scale enterprise workflows, but they also can introduce new complexities for IT teams, such as how to secure partner or vendor identities in addition to the rest of their enterprise users. Workforce Identity Federation can enable enterprises to selectively federate users from partner or vendor IdPs without requiring enterprise IT teams to sync or create a separate identity store to use Google Cloud resources.One common scenario where Workforce Identity Federation can help is when a company hires a partner or vendor to provide outsourced development services using cloud resources (such as when Google Kubernetes Engine (GKE) DevOps services are outsourced to a partner.) The company creates a separate workforce pool for the partner or vendor’s administrator, who can then use their own IdP to grant access to their workforce.This use case can also help support organizations who have requirements to store and maintain identity information locally in support of data residency or digital sovereignty initiatives. By using a local IdP, either customer-managed or partner-managed, and federating identities to Google Cloud, organizations can further strengthen control over their identity information.Seamless experience for users, easy access management for administratorsBefore Workforce Identity Federation, organizations would need to duplicate user identities from their IdP by creating user accounts in Google Cloud Identity. Workforce Identity Federation can help you access Google Cloud without having to first create Cloud Identity user accounts. It also reduces toil by eliminating the need to maintain two separate identity management systems. Identity providers such as ForgeRock see tremendous value in the Workforce Identity Federation, and how Google Cloud can work with them to jointly help customers manage workforce identities. Peter Barker, ForgeRock’s Chief Product Officer, said that his company’s partnership with Google Cloud makes identity management easy and secure for administrators and users alike.“Our strategic partnership with Google Cloud delivers great value to our customers and we’re excited to continue to expand our relationship. This integration with Google Cloud Workforce Identity Federation enables ForgeRock customers to leverage their current IAM investments and makes it easier for employees, contractors, and partners to securely access Google Cloud resources.”Getting started with Workforce Identity FederationWorkforce Identity Federation is now available in Preview to customers already using Google Cloud. You can learn more about Workforce Identity Federation by visiting our webpage and watching this video.Please contact your account manager to see if workforce identity federation is the right fit for your organization. And, you can get started with these new capabilities today using our product documentation.Related ArticleIntroducing on-demand backup, schema extension support for Google Cloud’s Managed Microsoft ADSchema extension and on-demand backup/restore are now available with Managed Microsoft AD.Read Article
Quelle: Google Cloud Platform
We are excited to announce that Cloud Spanner now supports 40,000 mutations per commit, double the existing limit at no additional cost. Cloud Spanner is a globally replicated, highly available, externally consistent ACID-compliant database. Customers across multiple sectors, including financial services and gaming, rely on externally consistent inserts, updates and deletes at scale to deliver fast and accurate experiences. A mutation represents a sequence of inserts, updates, and deletes that Spanner applies atomically to different rows and tables in a Spanner database. Cloud Spanner places limits on the number of mutations that can be included in a single transaction to ensure fast and consistent writes. Previously, queries were limited to 20,000 mutations per transaction, whether you were using DML via SQL or the lower-level Mutation API. We’ve doubled this limit to 40,000 to simplify batch updates and deletes. This is available to all customers of Cloud Spanner today. The size limit for each transaction remains unchanged at 100MB.What are mutations?Mutations are changes to values in the database. Cloud Spanner provides multiple ways to update data Standalone DML statements in transactions Batch DML statements to reduce the number of calls (and hence, round-trip latency) to the Spanner front-end Partitioned DML to automatically divide a large update into multiple transactions The programmatic Mutation API to change one or more cells at a time. A cell is an intersection of a row and a column. The API computes the cells to be updated from the user-provided rows and columns. Mutations across these approaches aggregate into the same mutations per transaction limit mentioned above. In the case of PartitionedDML, the mutation limit is not applied to the query itself, but Spanner enforces this limit when it creates the multiple transactions. For the other approaches, the user takes the responsibility. A single transaction may contain a combination of standalone and batch DML statements, in addition to programmatic API calls. Remember though, changes made with DML statements are visible to the subsequent statements.The DML or Mutation API describes the primary table that is impacted by the mutation. Interleaved tables and indexes on the affected columns also need to be updated as part of this transaction. These additional locations are referred to as effectors. You can think of effectors as those tables that are affected by the mutations, in addition to the target of the mutation. The mutation limit includes the changes to these effectors.Change streams watch updates in Spanner tables and write records of what changed elsewhere in the database in the same transaction. These writes are not included in the mutation limit.How can I estimate mutation counts?Spanner returns the mutation counts as part of the commit response message for a transaction. You can also estimate the number of mutations in a transaction by counting the number of unique cells updated across all the tables, including secondary indexes, as part of the transaction. Remember that a cell is the intersection of a row and column, like in a spreadsheet. A table that contains R rows and C columns, has R * C cells. Inserting a new row counts asC mutations since there are C cells in each row. Similarly, updating a single column counts as R mutations.More formally, if a commit consists of inserts to a primary table and one or more secondary indexes, the formula for calculating the number of mutations per commit is as follows.where R = number of rows/objects being inserted,C = number of columns updated as part of the transaction.Ii = number of secondary indexes on the current column.In other words, for each row, the update writes C cells in the primary table and one cell for each of the secondary indexes hanging off of the columns. For example, if an update touches 4 columns (regardless of the number of columns in the row) over 10 rows and two of those columns have secondary indexes, plugging into the formula above,where Ii is 1 for each of the 2 columns with secondary indexes and 0 for others. 4 * 10 + 2 * 10 = 60 mutations. Deletes are counted differently when it comes to logically adjacent cells. These are cells that are placed next to each other in table ordering and memory. Most common examples are logically adjacent cells are:Columns in a single rowConsecutive rowsInterleaved tablesDeletion of these cells count as a single mutation. So, deleting an entire row counts as one mutation. Deletions of cells that are not placed together, are still counted the same as insertions above. This means deletions of secondary indexes and foreign keys will count as one per cell. Deleting a column counts as R mutations, not including index deletions/changes.Is there a cost to using larger mutations?Transactions with more mutations involve more work. Since Spanner scales horizontally, much of this work can be distributed across multiple nodes. If this additional work causes instances to run hot, it may impact tail latencies for your application. Larger transactions need more memory and more compute cycles to write the additional bytes to disk. Mutations that are spread across the key space span multiple database splits. The transactions are guaranteed to be externally consistent but may take longer to complete. Account for these factors when constructing your transactions and scale up your instances to handle the additional load. Luckily Spanner can scale up or down in minutes without downtime and the compute capacity is prorated.Tip: When the number of mutations in a transaction is doubled, the transaction size doubles as well if no other changes are made.Mutations spread out across the key space or involving many indexes require coordination between the nodes (to maintain consistency). More specifically, different portions of the key space may be in different Paxos groups. In Spanner, each Paxos group achieves consensus through quorum. Reaching quorum in multiple Paxos groups takes time and the transaction will need to abort if any one of the Paxos groups is unable to reach quorum. Transactions with more mutations are more likely to include more Paxos groups.To summarize, large mutations are more resource intensive and can have measurably higher latencies. You can ameliorate these effects by reducing the size of the transaction and reducing the key-range of the mutations.What’s next?Congratulations! You learnt the different ways to write mutations, how to count them and how to compose transactions such that you can do more work in each transaction. Here are some things you should consider.If your application would benefit from the larger 40,000 mutation limit, increase the number of mutations in each transaction. Monitor the CPU usage and latencies to ensure that your instances are able to handle the additional load.Add more nodes, reduce transaction size and/or key space range for the transaction to improve these metrics.You can get started today for free with a 90-day trial or for as low as $65 USD per month.Related ArticleSpanner on a modern columnar storage engineGoogle’s planet-scale database, Spanner, was migrated to a modern columnar storage engine with many critical services running on top unin…Read Article
Quelle: Google Cloud Platform
