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At Google I/O this year, we introduced Vertex AI to bring together all our ML offerings into a single environment that lets you build and manage the lifecycle of ML projects. In a previous post, we gave you an overview of Vertex AI, sharing how it supports your entire ML workflow—from data management all the way to predictions. Today, we’ll talk a little about how to manage ML datasets with Vertex AI.Many enterprises want to use data to make meaningful predictions that can bolster their business or help them venture into new markets. This often requires using custom machine learning models—something not every business knows how to create or use. This is where Vertex AI can help. Vertex AI provides tools for every step of the machine learning workflow—from managing data sets to different ways of training the model, evaluating, deploying, and making predictions. It also supports varying levels of ML expertise, so you don’t need to be an ML expert to use Vertex AI.Types of data you can use in Vertex AIDatasets are the first step of the machine learning lifecycle—to get started you need data, and lots of it. Vertex AI currently supports managed datasets forfour data types—image, tabular, text, and videos. ImageImage datasets let you do:Image classification—Identifying items within an image.Object detection—Identifying the location of an item in an imageImage segmentation—Assigning labels to pixel level regions in an image.To ensure your model performs well in production, use training images similar to what your users will send. For example, if users are likely to send low quality images, be sure to have blurry and low resolution images in your data set. Don’t forget to include different angles, backgrounds, and resolutions. We recommend you include at least 1,000 images per label (item you want to identify), but you can always get started with 10 per label. The more examples you provide, the better your model will be.TabularTabular datasets enable you to do:Regression—Predicting a numerical value.Classification—Predicting a category associated with a particular example.Forecasting—Predicting the likelihood of sudden events or demands.Tabular data sets support hundreds of columns and millions of rows. TextWith text datasets, you can do:Classification—Assigning one or more labels to an entire document.Entity extraction—Identifying custom text entities within a document, like “too expensive” or “great value”.Sentiment analysis—Identifying the overall sentiment expressed in a block of text, for example, if a customer was happy or upset or frustrated.VideoVideo datasets enable:Classification—Labeling entire videos, shots, or frames.Action recognition—Identifying clips video clips where specific actions occur.Object tracking—Tracking specific objects in a video.Creating and managing datasets in Vertex AINow that we’ve covered the different types of data you can use, let’s shift to creating and managing those datasets. In the Cloud Console, go to Vertex AI dashboard page and click Datasets, then click Create Project.Say you want to classify items within a set of photos. Create an image dataset and select image classification. You can import files directly from your computer, which will be stored in Cloud Storage. Then, you’ll need to add the corresponding labels (items you want to identify) for your images. If you already have labels, you can use the Import File option to import a CSV with your image URLs and their labels. If your data is not labeled and you would like human help to label it, you can use the Vertex AI data labeling service. Once the files are uploaded, you can create labels and assign them to the images. You can also analyze the images in the data set, the number of images per label, and a few other properties. Depending on the type of data you use, your options might vary slightly. For example, if you want to use tabular data, you could upload a CSV file from your computer, use one from Cloud Storage, or select a table from BigQuery directly. Once you select the table, the data is available for analysis.More to comeThis concludes our overview of creating and managing datasets in Vertex AI. In a future installment, we’ll go over the next phase of the machine learning workflow: building and training ML models. If you enjoyed this post, keep an eye out for more AI Simplified episodes on YouTube. In the meantime, here’s where you can learn more about Vertex AI.Related ArticleWhat is Vertex AI? Developer advocates share moreDeveloper Advocates Priyanka Vergadia and Sara Robinson explain how Vertex AI supports your entire ML workflow—from data management all t…Read Article
Quelle: Google Cloud Platform

8. Juni 2021

da Agency

What’s next for SAP on Google Cloud—at SAPPHIRE NOW and beyond

The past year has been a period of rapid change for most businesses. The COVID pandemic, growing focus on sustainability, supply-chain disruptions, and other crises challenged all of us to adapt, evolve, and step up the pace of innovation.Today, we can see the results everywhere: a rapid transformation of the workplace, a raised bar for digital customer experiences, and a greater emphasis on deriving value from businesses’ IT investments. For businesses running SAP—more than 90 percent of Forbes Global 2000 companies—this has meant accelerating their investments in the cloud in both large-scale and incremental ways.We are excited to strengthen our strategic relationship with Google Cloud to empower our employees with cloud productivity solutions, and to ensure that our most critical business systems and applications are delivered securely, efficiently, and sustainably,” said Dani Brown, senior vice president and CIO at Whirlpool Corporation.We’re proud that Google Cloud is supporting many of these businesses like Whirlpool Corporation, including bringing Vodafone’s SAP environment into the cloud, and significantly scaling other SAP customers’ core SAP workloads, as well as helping to modernize ERP systems with SAP on Google Cloud, and more.As SAP’s SAPPHIRE conference begins this week, we believe businesses have a more significant opportunity than ever to build for their next decade of growth and beyond. Here are several ways we’re working together with our customers, SAP, and our partners to support this transformation.Supporting ‘RISE with SAP’Google Cloud is partnering with SAP on its ‘RISE’ initiative, launched earlier this year to help accelerate customers’ business transformations with SAP in the cloud. We’re closely aligned with SAP’s approach, and we’re delighted to work together to make it very simple for customers to move applications and systems into the cloud while minimizing risk and cost, and creating fast time-to-value.By providing a streamlined path to SAP in the cloud—whether in a private cloud or hybrid environment, or a full-scale cloud migration—we’re helping customers more quickly benefit from flexible and scalable cloud infrastructure, leading AI, ML, and analytics capabilities, Live Migration capabilities to eliminate downtime, and services such as our Cloud Acceleration Program (CAP). CAP provides SAP customers with solutions from both Google Cloud and our partners to simplify migrations. Google Cloud also offers financial incentives to defray infrastructure costs and help customers ensure that duplicate costs are not incurred during migration.Turning SAP data into valuable insightsOrganizations’ business systems like SAP typically contain vast troves of valuable data, which often goes untapped. Today, we offer connectors that enable customers to very easily bring data from SAP Enterprise applications, SAP HANA, and from other sources into BigQuery which helps them collect, integrate, analyze, and manage data across single or mult-icloud environments. Bringing this data onto Google Cloud, and into BigQuery enables customers to not only store and gain rapid insights on large volumes of live data but also visualize insights in Looker to ultimately inform important business decisions. “We’re more efficient with our SAP data because, now, we’re leveraging BigQuery as our enterprise data warehouse,” says Sam Moses, Vice President of Corporate Systems at The Home Depot. “On top of that, now we have better and faster access from a data and analytic standpoint, so our business can make decisions just a lot faster.”Learn more about The Home Depot at SAPPHIRE in our fireside chat with Sam Moses and Abdul Razack, Vice President, Solutions Engineering, Technology Solutions and Strategy at Google Cloud. BigQuery makes SAP data smarter and more valuableFor more advanced analytics needs, many of our SAP customers derive extraordinary benefits from smart analytics—working with BigQuery’s AI and ML capabilities, discovering the power of predictive analytics, and processing multi-petabyte datasets faster than previously possible. “We now have at our fingertips a lot of new technologies such as BigQuery, that we’ve never been exposed to” says Joe Schleupner, senior director of PMO & ITS planning and implementation at Southwire. “We’re able to rapidly detect patterns, gather insights from our data, come up with new solutions, and figure out the art of the possible.”One of our top priorities is to give customers the tools they need to get their SAP data into BigQuery. Whether that data lives in Google Cloud, on premises, or in another public cloud, customers can easily import data into BigQuery through Google Cloud’s BigQuery connector for SAP enabling near-real time analysis of their data sets. This connector will give our SAP customers a faster and more reliable source of data-driven insights and lay the foundation for smarter business decisions.Innovating our technology and expanding our work with partnersGoogle Cloud continues to build new capabilities and features to enable SAP customers to digitally transform on a smarter cloud. We’re also working harder than ever with our ecosystem of partners, to help customers’ SAP environments scale, unlock new sources of value, and stay secure on Google Cloud. Persistent disk balances storage cost and performanceGoogle Cloud’s new Balanced Persistent Disk (PD) offering allows SAP customers to balance SSD-based PD cost and performance by running business-critical application servers at a nearly 60 percent lower cost per GB than the SSD storage required to run a production HANA system. And all Google Cloud SSD storage now supports read/write parity, so that storage engineers no longer have to calculate differences between read IOPS and write IOPS when provisioning SAP storage infrastructure.The largest compute configurations to help scale HANANew compute families for SAP customers running HANA OLTP and OLAP environments are making it easier to scale without sacrificing cost or performance. These options include 12-TB scale-out solutions and 18-TB and 24-TB scale-up solutions, and 6TB VM and 12TB OLAP solutions—all of which offer affordable and practical paths to long-term growth.Apigee gives SAP customers a secure API onramp to enable reliable digital experiencesOur Apigee API management solution gives SAP customers a reliable and scalable way to manage API-based integration with their SAP systems and get value from business data to meet the new and changing demands of the digital economy. Apigee API managementallows customers to monitor and monetize APIs which can be beneficial for federated or shared services business environments. Lastly, Apigee API Management abstracts SAP interfaces to enable a seamless migration of the underlying SAP system to the cloud without disrupting the surrounding systems that rely on SAP data. Actifio creates a safer migration path for SAP customersLate in 2020, Google Cloud announced the acquisition of Actifio, a leader in backup and disaster recovery (DR) for SAP environments to help SAP customers move faster and reduce risk during the migration process. We’re using Actifio to power our new SAP Migration Quickstart offering. We work with customers to generate a production-like copy of their SAP environment with Actifio and migrate this snapshot from their on-prem environment to Google Cloud.Our SAP partners are stepping up and standing outNow more than ever, our partners are stepping up at critical points in the cloud journey to solve implementation challenges, promote interoperability and integration with SAP, and find faster routes to ROI with Google Cloud.Making a difference with Smart Analytics: A number of Google Cloud partners, including Informatica, Qlik, Datavard and SoftwareAG continue to raise the bar on innovation, flexibility, and performance with Google Cloud’s analytics platforms. They’re also showing how data analytics can make a difference far beyond a company’s balance sheet. We see this, for instance, in Qlik’s efforts to help its customers use SAP data and Google Cloud analytics to support environmental sustainability initiatives.Unlocking new sources of value within SAP: Since being selected as Google Cloud’s Information Management (IM) solution, OpenText has proven its value for SAP customers. For example, it manages unstructured document data bringing that information directly to the users in any SAP Business process. It also transforms unstructured document data into a rich, new source of analytical value and drives big efficiency gains, cost savings, and reducing risks. By doing this, OpenText is giving our SAP customers some powerful new ways to become intelligent enterprises.Join us on the journey aheadThere’s so much more to come from SAP and Google Cloud. Join us in our virtual booth this week at SAPPHIRE (register here first) to see Google Cloud’s continued commitment to simplifying and optimizing customers’ journeys to the cloud, and to take a dive deeper into why Google Cloud gives SAP customers a level of performance, reliability, and value that puts them at an advantage. Learn more about SAP on Google Cloud and hear more from customers about their SAP on Google Cloud deployments.Related Article6 businesses transforming with SAP on Google CloudBusinesses globally are running SAP on Google Cloud to take advantage of greater agility, uptime, and access to cutting edge smart analyt…Read Article
Quelle: Google Cloud Platform

8. Juni 2021

da Agency

Node, Python and Java repositories now available in Artifact Registry

As a developer, you need a secure place to store all your stuff: container images of course, but also language packages that can enable code reuse across multiple applications. Today, we’re pleased to announce support for Node.js, Python and Java repositories for Artifact Registry in Preview. With today’s announcement, you can not only use Artifact Registry to secure and distribute container images, but also manage and secure your other software artifacts. At the same time, the Artifact Registry managed service provides advantages over on-premises registries. As a fully serverless platform, it scales based on demand, so you only pay for what you actually use. Enterprise security features such as VPC-SC, CMEK, and granular IAM ensure you get greater control and security features for both container and non-container artifacts. You can also connect to tools you are already using as a part of a CI/CD workflow. Let’s take a closer look at the features you’ll find in Artifact Registry, giving you a fully-managed tool to store, manage, and secure all your artifacts. Expanded repository formatsWith support for new repository formats, you can streamline and get a consistent view across all your artifacts. Now, supported artifacts include:Java packages (using the Maven repository format)Node.js packages (using the npm repository format)Python packages (using the PyPI repository format)In addition to existing container images and Helm charts (using the Docker repository format). Easy integration with your CI/CD toolchainYou can also integrate Artifact Registry, including the new repository formats, with Google Cloud’s build and runtime services or your existing build system. The following are just some of the use cases that are made possible by this integration:Deployment to Google Kubernetes Engine (GKE), Cloud Run, Compute Engine and other runtime services CI/CD with Cloud Build, with automatic vulnerability scanning for OCI images Compatibility with Jenkins, Circle CI, TeamCity and other CI tools Native support for Binary Authorization to ensure only approved artifact images are deployedStorage and management of artifacts in a variety of formatsStreamlined authentication and access control across repositories using Google Cloud IAMA more secure software supply chainStoring trusted artifacts in private repositories is a key part of a secure software supply chain and helps mitigate the risks associated with using artifacts directly from public repositories. With Artifact Registry, you can:Scan container images for vulnerabilitiesProtect repositories via a security perimeter (VPC-SC support)Configure access control at the repository level using Cloud IAMUse customer managed encryption keys (CMEK) instead of the default Google-managed encryptionUse Cloud Audit Logging to track and review repository usageOptimize your infrastructure and maintain data complianceArtifact Registry provides regional support, enabling you to manage and host artifacts in the regions where your deployments occur, reducing latency and cost. By implementing regional repositories, you can also comply with your local data sovereignty and security requirements.Get started todayThese new features are available to all Artifact Registry customers. Pricing for language packages is the same as container pricing; see the pricing documentation for details.To get started using Node.js, Python and Java repositories, try the quickstarts in the Artifact Registry documentation.Node.js Quickstart GuidePython Quickstart GuideJava Quickstart GuideVideo Overview: using Maven in Artifact RegistryRelated ArticleHow we’re helping to reshape the software supply chain ecosystem securelyWe’re sharing some of the security best practices we employ and investments we make in secure software development and supply chain risk …Read Article
Quelle: Google Cloud Platform

8. Juni 2021

da Agency

Google Dataflow is a Leader in The 2021 Forrester Wave™: Streaming Analytics

We are excited to announce that Google has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report. Thank you to our strong community of customers and partners for working with us to deliver a customer focused product. We believe Forrester’s recognition is an acknowledgement of our leadership across an integrated set of capabilities that rely on data to drive transformation. We were also honored to be named a leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021. Forrester gave Dataflow a score of 5 out of 5 across 12 different criteria and according to the report: “Google Cloud Dataflow has strengths in data sequencing, advanced analytics, performance, and high-availability. Google Dataflow’s sweet spot is for enterprises that have a preponderance of real-time data generated on Google Cloud Platform or wish to simplify all data processing by using a single platform that unifies both streaming and batch jobs.” Harnessing the power of real-time dataThe speed with which businesses are able to respond to change is the difference between those that successfully navigate the future and those that get left behind. In order to accelerate their digital transformation, reimagine their business and leverage the power of real-time data, today’s data leaders require a streaming analytics platform that provides both depth and breadth. Cloud Pub/Sub and Cloud Dataflow, based on more than a decade of experience in internet scale systems for Google’s own needs, provide customers with a reliable, scalable, performant platform. In addition, we’ve designed these products for ease of use to make streaming analytics accessible to more users, which is why customers such as Sky and others from across all industries use Dataflow to run streaming analytics workloads.5 out of 5 across key streaming analytics criteriaWhile Forrester gave Dataflow a score of 5 out of 5 in 12 criteria, the product achieved the highest possible scores in areas that are top of mind for our customers.We continue to be focused on solving problems that matter to you. For example, just in the last month we announced Dataflow Prime and Auto Sharding for BigQuery – two new auto tuning capabilities that bring efficiency and simplicity to your streaming pipelines. Dataflow achieves highest score possible in strategy With Google, organizations gain an industry leading product and a partner that has the vision and strategy to help you tackle new business challenges and provide delightful experiences to your customers.In summary, we are honored to be a Leader in The Forrester Wave™, Streaming Analytics, and look forward to continuing to innovate and partner with you on your digital transformation journey. Download the full report: The Forrester Wave™: Streaming Analytics, Q2 2021 and check out these smart analytics reference patterns. To learn more about Dataflow, visit our website and get to know the product by taking an interactive tutorial. You can also watch recordings from the Data Cloud Summit event (May 2021), where we provided an in-depth view of new product innovations in Dataflow and other data analytics products.Related ArticleGartner 2020 Magic Quadrant for Cloud Database Management Systems names Google a LeaderGartner’s first-ever database management systems (DBMS) Magic Quadrant (MQ) names Google Cloud a Leader.Read Article
Quelle: Google Cloud Platform