Kategorie: Google Cloud Platform

Anthos makes multi-cloud easier with new API, support for Azure

One of the main reasons organizations adopt Anthos is to simplify the management of Kubernetes-based applications across a variety of clouds. And now, with our latest release, we’ve made multi-cloud management even easier with the general availability of the Anthos Multi-Cloud API.In addition, in this latest release,Support for Anthos clusters1 running on Azure is also now generally availableWe’ve added integrated logging and monitoringWe’ve introduced Connect Gateway support for unified cluster access, with Terraform and Kubernetes Config Connector support coming soon! Let’s take a deeper look at what you can find in our latest Anthos release. Exploring the Multi-Cloud APIWith the latest release of Anthos, we’ve trimmed our installation footprint and streamlined our cluster management technology to allow you to use a single API for full lifecycle management of Anthos clusters running in AWS or Azure. Compare that to previous releases, which required you to install a management cluster in each cloud. Now, the Anthos Multi-Cloud API, the Google Cloud control plane does all the work! This release standardizes the gcloud CLI for deploying Anthos clusters in AWS, Azure, and GCP (with full Terraform support on the way). Clusters you create in other clouds appear in the Google Cloud Console, creating a centralized management view complete with cluster telemetry and logging. Now, creating a new Anthos cluster on Google Cloud, AWS or Azure is a simple gcloud command:Here’s the associated view from the Cloud Console:The Multi-Cloud API performs authentication with each cloud via service account or application registration, and allows clusters to be deployed on existing or newly created VPCs/Vnets. It supports multiple machine types in each cloud, with plans to support even more soon (AWS, Azure). As a reminder, Anthos clusters on Azure or AWS integrate with each respective cloud’s native KMS, storage facilities, and load balancing. Using Connect Gateway to connect to Anthos clusters in AWS and AzureConnect gateway allows you to interact with your Anthos clusters securely, and now it works with Anthos clusters running on AWS and Azure too. Cluster commands are routed through a GCP Service to your clusters over an encrypted connection, removing the need for end users to use a VPN. Putting together a multi-cloud strategyOperationalizing Google-managed Kubernetes clusters in all three major clouds is now much easier with the release of the Multi-Cloud API. The next step is to apply configuration governance and policy controls to the clusters which will create safe and secure deployment landing zones for your applications regardless of the environment.For one thing, you can now leverage Anthos Configuration Management (ACM), which automates policy and security at scale for Kubernetes clusters whether they are running on-premises, on GCP, and on other public clouds. ACM synchronizes your clusters to a git repository that contains your business specific configurations and policies. Developers can launch their applications by adding configuration files to the ACM repo or they can use their existing CD tooling. In either case, by using ACM, you can be sure security and governance is applied uniformly across your fleet of clusters.Meanwhile, Cloud Run for Anthos and Anthos Service Mesh offer tremendous value to organizations looking to optimize and secure Kubernetes-based workloads. Cloud Run for Anthos enables container-based application deployments that scale to zero with predictable costs in your own clusters while making use of existing CI/CD pipelines and security tooling. Anthos Service Mesh brings advanced application networking capabilities to your services and valuable inter-cluster communication telemetry, and is designed to work on Anthos clusters running on GKE, AWS and Azure. These Anthos capabilities are critical to businesses that manage microservice-based applications at scale; look for them to be released in the coming in the coming monthsGet started todayAnthos clusters are enterprise-grade Kubernetes clusters that are entirely supported by Google Cloud — and now running them in AWS and Azure is a seamless experience. To get started, check out our Install Anthos Clusters on AWS or Azure guide.1. An Anthos cluster refers to a Google-managed Kubernetes cluster that can run outside of Google Cloud.
Quelle: Google Cloud Platform

Unlock the power of change data capture and replication with new, serverless Datastream, now GA

We’re excited to announce that Datastream, Google Cloud’s serverless change data capture (CDC) and replication service, is now generally available. Datastream allows you to synchronize data across disparate databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. You can easily and seamlessly deliver change streams from Oracle and MySQL databases into Google Cloud services such as BigQuery, Cloud SQL, Google Cloud Storage and Cloud Spanner, saving time and resources and ensuring your data is accurate and up to date. Get started with Datastream today.Datastream provides an integrated solution for CDC replication use cases with custom sources and destinations*Check the documentation page for all supported sources and destinations.Since our public preview launch earlier this year, we’ve seen Datastream used across a variety of industries, by customers such as Chess.com, Cogeco, Schnuck Markets, and MuchBetter. This early adoption strengthens the message we’ve been hearing from customers about the demand for change data capture to provide replication and streaming capabilities for real-time analytics and business operations. MuchBetter is a multi-award-winning e-wallet app, providing a truly secure and enjoyable banking alternative for customers all over the world. Working with Google Cloud Premier Partner Datatonic, they’re leveraging Datastream to replicate real-time data from MySQL OLTP databases into a BigQuery data warehouse to power their analytics needs. According to Andrew McBrearty, Head of Technology at MuchBetter, “from MuchBetter’s point of view, leveraging Dataflow, BigQuery and Looker has unlocked additional insights from our ever-increasing operational data. Using Datastream in our solution ensured continued real-time capability – we now have trend analysis in place, improved efficiency across the business, and the ability to use our data to derive actionable insights and to make data-driven decisions. This means we can continue to grow and adapt at a pace our customers have come to expect from MuchBetter. And for the first time, the world of ML and AI is open to us.”Getting to know DatastreamGoogle Cloud customers are choosing Datastream for real-time change data capture because of its differentiated approach:Simple experienceReal-time replication of change data shouldn’t be complicated: database preparation documentation, secure connectivity setup, and stream validation should be built right into the flow. Datastream delivers on this experience, as MuchBetter discovered during their evaluation of the product. “Datastream’s ease-of-use and immediate availability (serverless) meant we could start our evaluation and immediately see results”, says Mark Venables, Principal Data Engineer at MuchBetter. “For us, this meant getting rid of the considerable pre-work needed to align proof of concept tests with third-party CDC suppliers.”Datastream guides you to success by providing detailed pre-requisites and step-by-step configuration guidelines to prepare your source database for CDC ingestion.End-to-end solutionBuilding pipelines to replicate changes from your source database shouldn’t take up all of your team’s time. Use pre-built Dataflow templates to easily replicate data into BigQuery, Cloud Spanner or Cloud SQL. Out of the box, these Dataflow templates will automatically create the tables and update the data at the destination, taking care of any out-of-order or duplicate events, and providing error resolution capabilities. Leverage the templates’ flexibility to fine-tune Dataflow to fit your specific needs. “Google-managed Dataflow templates meant getting our pipelines up and running with minimal effort and fuss – this allowed more time to be spent on more complex pipeline development whilst tactically delivering solutions to our users,” says Venables.Secure Datastream keeps your migrated data secure, supporting private connectivity between source and destination databases. “Establishing connectivity is often viewed as hard. Datastream surprised us with its ease of use & setup, even in more secure modes,” says Grzegorz Dlugolecki, Principal Cloud Architect at Chess.com, a leading online chess community and mobile application, hosting more than ten million chess games every day. “Datastream’s private connectivity configuration allowed us to easily create a private connection between our source and the destination, and ensure our data is safe and secure.”Datastream provides a simple wizard to automatically set up private, secure connectivity to your source databaseHigh throughput, low latencyWith Datastream’s serverless architecture, you don’t need to worry about provisioning, managing machines, or scaling up resources to meet fluctuations in data throughput. Datastream guarantees high performance – a single stream can process 10’s of MBs per second, while ensuring minimal latency. “We evaluated several market-leading ETL solutions”, says Dlugolecki,  “Datastream was the only tool able to successfully sync our complex, single-table datasets, doing this in weeks instead of years estimated by the other vendors.”Getting started with DatastreamYou can start streaming real-time changes from your Oracle and MySQL databases today using Datastream:Navigate to the Datastream area of your Google Cloud console, under Big Data, and click Create Stream.Choose the source database type, and see what actions you need to take to set up your source.Create your source connection profile, which can later be used for additional streams.Define how you want to connect your source.Create and configure your destination connection profile.Validate your stream and make sure the test was successful. Start the stream when you’re ready.Once the stream is started, Datastream will backfill historical data and will continuously replicate new changes as they happen. Learn more and start using Datastream todayDatastream is now generally available for Oracle and MySQL sources. Datastream supports sources both on-premises and in the cloud, and captures historical data and changes into Cloud Storage. Integrations with Cloud Data Fusion and Cloud Dataflow (our data integration and stream processing products, respectively) replicate changes to other Google Cloud destinations, including: BigQuery, Cloud Spanner, and Cloud SQL.For more information, head on over to the Datastream documentation, see our step-by-step Datastream + Dataflow to BigQuery tutorial, or start training with this Datastream Qwiklab.Related ArticleUsing Datastream to unify data for machine learning and analyticsWhile machine learning model architectures are becoming more sophisticated and effective, the availability of high-quality, fresh data fo…Read Article
Quelle: Google Cloud Platform

How data and AI can help media companies better personalize; and what to watch out for

Media companies now have access to an ever-expanding pool of data from the digitally connected consumer. And over the past two years, as content consumption and audience behaviors have shifted in response to the world around us, direct-to-consumer has only accelerated. As media organizations pivot from third-party to first-party data, this presents challenges with the volume, velocity and fragmentation of data. It’s also an opportunity to better understand how to acquire, engage and retain audiences ⁠— and inject agility into their business amidst a competitive landscape. How should media companies be thinking about their data, and its value, to capitalize on this opportunity?  To help answer these questions, we sat down with Gloria Lee, Executive Account Director in Media & Entertainment and John Abel, Technical Director for the Office of the CTO at Google Cloud.Data and the growing importance of personalization There’s no doubt customer needs and expectations are in a constant state of flux. Across the media industry, audiences are increasingly expecting personalized content. In fact, a  PWC study conducted in 2020 found that nearly one-third (31%) of survey respondents said easy, personalized content recommendations would be a reason for staying with a streaming service.Audience engagement is the currency, and in a crowded space where attention is finite, media companies need a granular understanding of their audience. To do this, there’s an opportunity to capture and capitalize on first party data, so they can better serve their audience. Not just what audiences are consuming, but also when, where, and on which platform (and increasingly, those platforms are digital). These data points are key in understanding audiences deeply to deliver hyper-personalized experiences that audiences are expecting.    “If you look across the world today, we know that through digitalization, [that] hyper-personalization is required,” says John. “So that hyper-personalization, the volume of data and the value of the data is super critical across all industries. Media and entertainment is no different,” he adds.Enriching storytelling through AI & ML   Extracting insights on how, where and when the consumer wants to receive content will accelerate the need for data research; AI and ML will be critical to unlocking data’s full potential. “The most valuable data is generated data, typically from machine learning or AI, where you’re seeing new insights in data that give you new opportunities.” explains Gloria.  New technologies are providing insights — often in real time — about audiences, making personalization an easier task. An example use case would be recommending a new song based on a user’s listening history. This kind of personalization is just the start, as AI/ML unlocks more novel opportunities. For example, AL/ML can also be used to enrich the watching experience by finding opportune moments to integrate brands. As Gloria puts it, “Artificial intelligence, and machine learning is what enables people to quickly look through their content to find relevant moments for marketing purposes”. Getting personalization right, while making sure to keep consumer information safe and private is a challenge for all consumer companies; not just M&E. John explains “there’s a blend of how they move technology to the edge and they don’t break privacy.” Media and Entertainment companies will need to keep their data secure and private, using sophisticated practices like data federation. In this model, individual data is not exchanged.  Rather, data is first aggregated into cohorts to anonymize the individual. The goal of methods like this is to obtain useful insights while retaining privacy and security.How data is driving audience experiences Spotify is a prime example of a media company using data-led insights to provide personalized content for their customers — making it easier for users to discover new audio content and connect with their favorite artists or podcasts. “[With] Google Cloud…we can iterate quicker on key needs, like data insights and machine learning…[streamlining] our ability to concentrate on what’s important to our users and give them the experiences they know and love about Spotify.”—Tyson Singer, VP of technology and platform at SpotifySky, one of Europe’s  leading broadcasters is also transforming its data strategy to better serve their customers. By creating a scalable cloud-based architecture, Sky can keep up with increasing amounts of TV box diagnostic data on service uptime and delivery ⁠— meaning less data lost and more time to focus on improving user experience through personalization. “The data will sit right at the heart of Sky’s future strategy. It will help ensure that our products are intuitive and easy to use and that we can keep seamlessly connecting customers with the content and services they know and love,” says Oliver Tweedie, Director of Data Engineering at Sky.Transforming to a data-oriented Media companyKeeping up with new technology trends, inside and outside of the industry, will play a critical role in how media and entertainment companies can survive and thrive into the future. And without a way to centralize and draw insights from their data quickly, media organizations will struggle to stay in the race. With any type of change comes resistance. But at the end of the day, it all comes down to people. When navigating digital transformations, Gloria touches on the three categories of people: ⁠supporters, those excited about the change, those who couldn’t care less and detractors, those who are opposed to it. “It’s really tapping into the leaders for those three different groups within the company and trying to get them on board and seeing what their drivers are,” Gloria explains.So what advice do John and Gloria have for Media players looking into the data-led future?Related ArticleRead Article
Quelle: Google Cloud Platform

Google showcases Cloud TPU v4 Pods for large model training

Recently, models with billions or trillions of parameters have shown significant advances in machine learning capabilities and accuracy. For example, Google’s LaMDA model is able to engage in a free-flowing conversation with users about a large variety of topics. There is enormous interest within the machine learning research and product communities in leveraging large models to deliver breakthrough capabilities. The high computational demand of these large models requires an increased focus on improving the efficiency of the model training process, and benchmarking is an important means to coalesce the ML systems community towards realizing higher efficiencies.In the recently concluded MLPerf v1.1 Training round1, Google submitted two large language model benchmarks into the Open division, one with 480 billion parameters and a second with 200 billion parameters. These submissions make use of publicly available infrastructure, including Cloud TPU v4 Pod slices and the Lingvo open source modeling framework. Traditionally, training models at these scales would require building a supercomputer at a cost of tens or even hundreds of millions of dollars – something only a few companies can afford to do. Customers can achieve the same results using exaflop-scale Cloud TPU v4 Pods without incurring the costs of installing and maintaining an on-premise system. Large model benchmarksGoogle’s Open division submissions consist of a 480 billion parameter dense Transformer-based encoder-only benchmark using TensorFlow and a 200 billion-parameter JAX benchmark. These models are architecturally similar to MLPerf’s BERT model but with larger dimensions and number of layers. These submissions demonstrate large model scalability and high performance on TPUs across two distinct frameworks. Notably, these benchmarks, with their stacked transformer architecture, are fairly comparable in terms of their compute characteristics with other large language models.Figure 1: Architecture of the Encoder-only model used in Google’s MLPerf 1.1 submissions.Our two submissions were benchmarked on 2048-chip and 1024-chip TPU v4 Pod slices, respectively. We were able to achieve an end-to-end training time of ~55 hours for the 480B parameter model and ~40 hours for the 200B parameter model. Each of these runs achieved a computational efficiency of 63%- calculated as a fraction of floating point operations of the model together with compiler rematerialization over the peak FLOPs of the system used. Next-generation ML infrastructure for large Model training Achieving these impressive results required a combination of several cutting edge technologies. First, each TPU v4 chip provides more than 2X the compute power of a TPU v3 chip – up to 275 peak TFLOPS. Second, 4,096 TPU v4 chips are networked together into a Cloud TPU v4 Pod by an ultra-fast interconnect that provides 10x the bandwidth per chip at scale compared to typical GPU-based large scale training systems. Large models are very communication intensive: local computation often depends on results from remote computation that are communicated across the network. TPU v4’s ultra-fast interconnect has an outsized impact on computational efficiency of large models by eliminating latency and congestion in the network.Figure 2: A portion of one of Google’s Cloud TPU v4 Pods, each of which is capable of delivering in excess of 1 exaflop/s of computing power.The performance numbers demonstrated by our submission also rely on our XLA linear algebra compiler and leverage the Lingvo framework. XLA transparently performs a number of optimizations, including GSPMD based automatic parallelization of many of the computation graphs that form the building blocks of the ML model. XLA also allows for reduction in latency by overlapping communication with the computations. Our two submissions demonstrate the versatility and performance of our software stack across two frameworks, TensorFlow and JAX.Large models in MLPerfGoogle’s submissions represent an important class of models that have become increasingly important in ML research and production, but are currently not represented in MLPerf’s Closed division benchmark suite. We believe that adding these models to the benchmark suite is an important next step and can inspire the ML systems community to focus on addressing the scalability challenges that large models present.Our submissions demonstrate 63% computational efficiency, cutting edge in the industry. This high computational efficiency enables higher experimentation velocity through faster training. This directly translates into cost savings for Google’s Cloud TPU customers.  Please visit the Cloud TPU homepage and documentation to learn more about leveraging Cloud TPUs using TensorFlow, PyTorch, and JAX.1. The MLPerf name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use is strictly prohibited. See www.mlcommons.org for more information.
Quelle: Google Cloud Platform

Shopify engineers deliver on peak performance during Black Friday Cyber Monday 2021

Many predicted this would be the biggest Black Friday/Cyber Monday (BFCM) weekend ever recorded. And it looks like those predictions were right — especially for independent and direct to consumer (DTC) brands. Shopify, a leading provider of essential internet infrastructure for commerce, works with more than 1.7 million merchants worldwide. Over the course of the long weekend, the company’s merchants welcomed a record number of consumers purchasing from independent and DTC brands (47 million globally) and drove $6.3 billion in global sales for a 23% increase y/y (up from $5.1 billion in global sales in 2020). These results are incredible on their own, and even more so when we remember that these increases are on top of last year’s pandemic-fueled shift to online shopping.But what does it take to handle this type of scale? Noted across the industry for their innovative approaches to peak time shopping solutions, Shopify engineers are leveraging Google services to enhance performance for merchants and shoppers like never before. Shopify engineers are showing commerce how big event performance is doneShopify knows how critical peak seasons are for its merchants. With peak sales of more than $3.1 million per minute at 12:02 PM EST on Black Friday, November 26, Shopify leverages one of the most skilled teams of engineers to develop pioneering tools that improve scalability and velocity for merchants.“Achieving a record-breaking sales weekend for BFCM 2021 is only possible with an infrastructure that’s built for performance and scale,” said Delaney Manders, VP of Engineering for Shopify. “With the incredible collaboration between Shopify engineering and Google Cloud, we averaged about 30TB/min of egress traffic across our infrastructure and helped our merchants deliver near-perfect uptime for their consumers during peak sales periods.” Through customer experience and operational optimization tools designed with Google Cloud services, Shopify delivers streamlined and reliable performance for merchants and consumers during high traffic events. With tools like Shopify Inbox and Shop Pay, Shopify engineers have designed some of the finest merchant and consumer experience optimization tools in commerce. Through their solutions, Shopify engineers work smarter, not harder, and enable merchants to meet peak event demand without added hassle or stress. Black Friday/Cyber Monday may be the event of the season for many Shopify merchants but the massive scalability, low latency, and optimized uptime make high traffic and high risk events as easy to execute as any other day.From the merchant, to the backend, to the consumer, Shopify’s engineers have developed tools to optimize performance for merchants and shoppers, while simplifying their workloads. With these approaches, big event days at Shopify go as smoothly as any other.Shop Pay takes the lines (and the forms) out of shoppingFor the end customer, a time-consuming checkout experience can easily discourage or delay a purchase. When it comes to big events like Black Friday/Cyber Monday, Shopify recognizes the particular significance of an intelligent and seamless checkout experience. The company’s engineers developed Shop Pay, a solution that helps accelerate the purchase process. In addition to supporting faster checkout, Shop Pay also personalizes the shopping experience by remembering a shopper’s preferences and encrypting everything for optimal safety.Shopify’s data shows that Shop Pay increases checkout speed by 4x. Following an analysis of 10,000 of its largest merchants, Shopify found merchants who enabled Shop Pay had an average checkout-to-order rate 1.72x times higher than those going through regular checkouts. This innovation in platform performance significantly increased growth and retention for merchants. Shop Mover gets the backend ready for a crowd  To keep operations as agile and modern as possible, Shopify developed and open sourced a general purpose MySQL data migration tool, Ghostferry. Supported by Google Cloud services, Ghostferry moves data across different MySQL instances while the application is still running and with minimal downtime (<5 seconds). Shopify’s Shop Mover is built on top of Ghostferry and Google Cloud services, enabling load-balancing of data shards across multiple databases. In addition to being the tool used for the initial migration out of Shopify’s data centers and into Google Cloud, Shop Mover now moves hundreds of thousands of shops every year and is a reason Shopify merchants could handle the high volume of BFCM 2021. Shopify’s global reach means better deals for merchants and shoppers. To expand access to new regions while optimizing platform performance for merchants, Shopify engineers leveraged the global infrastructure of Google Cloud. Shopify supports merchants from around the world with Google Cloud’s network of 25 regions and 76 availability zones. With lower latency and higher reliability, performance is optimized for shoppers and merchants around the world. Through its partnership with Google, Shopify engineers are leveragingGlobal Virtual Private Cloud (VPC) to streamline the writing and deployment of applications that span multiple regions, and in-country disaster recovery which helps Shopify maintain business continuity across the globe.  The culture of creativity, scale, and velocity among Shopify’s engineering team continues to drive exciting solutions for enhanced peak event performances. Backed by fresh approaches to improving the customer experience and streamlining operations at scale, Shopify engineers can expect a streamlined workday and merchants can confidently deliver for shoppers without a hassle. And this translates to a better holiday shopping experience for merchants and shoppers alike.
Quelle: Google Cloud Platform

Learn how Notified accelerated discovery and classification of journalists at scale with Google Cloud AI

Notified is a leading communications cloud for events, public relations, and investor relations to drive meaningful insights and outcomes. They provide communications solutions to effectively reach and engage customers, investors, employees, and the media.One of Notified’s Public Relations solutions is the ‘Media Contact Database’ that allows customers to discover media and influencers in a unique media database powered by AI and human-curated research. The goal of the initiative is to expand the scope of the AI driven, dynamically discovered influencers, and analyze online news articles using AI/ML technologies to extract entities and classify content. The prior process to extract insights from news articles provided only 30-40% of the desired results, and there were accuracy and stability issues that resulted in a lot of manual intervention.Journalist BeatA key outcome of the AI driven process is to identify the ‘Journalist Beat’. A Journalist Beat essentially summarizes the individual’s area of focus such as a sports writer, financial journalist etc. Three options were evaluated for the AI/ML process to generate the Journalist Beats :Option 1:  Topic MLUnsupervised ML approach to determine the commonly used terms.Pro: Common approach to grouping documents and determine similar textCon: Unbounded list of textOption 2: ML ClassificationBuild classification models (supervised) to map reference articles to ‘Beats’ Pro: Aligns to ‘Research Analytics’ existing processesCon: Time to build and maintain ML models for hundreds of beats.Option 3: GCP Context ClassificationLeverage GCP’s Natural Language API for initial classification and as input to Notified single modelPro: Aligns to ‘Research Analytics’ without building ML models.Ultimately the GCP Natural Language API solution was chosen because of the speed of execution and a high level of accuracy with the pretrained models. The Notified team was able to launch the product feature within a few weeks, without ever needing to do extensive data collection and train the models. Here is the high level process that was implemented for Journalist Beats.Since Notified supports curated media contacts globally, news articles were instantly translated to English using GCP Translation API. GCP Natural Language API’s solution to classify text was used to analyze the translated text and generate the list of content categories.Solution ArchitectureHere is a sample solution architecture for the ‘Discovered Journalist’ process.Three core principles guided the above architecture – Serverless & Fully Managed, Scalability & Elasticity for flexibility and to optimize costs, API led real-time processing.In addition to the GCP Natural Language API and Translation API below are a few serverless GCP products that were part of the automated solution:BigQuery is Google Cloud’s fully managed, petabyte-scale, and cost-effective analytics data warehouse that lets you run analytics over vast amounts of data in near real time.Cloud Run is a fully managed serverless platform that can be used to develop and deploy highly scalable containerized applications.Cloud Tasks is a fully managed service that allows you to manage the execution, dispatch, and delivery of a large number of distributed tasks.The powerful pre-trained models of the Natural Language API provide a comprehensive set of features to apply natural language understanding to applications such as sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis. Notified looks ahead to super-scalingIn an effort to even further improve its best in class ‘Media Contact Database’, Notified looks to super scale the above AI driven Influencer Discovery process to the order of 100+ million news articles per month. It plans to expand the scope of entities extracted from the news articles and provide a news exploration service for its customers by performing intelligent entity-based searches.To watch your markets evolve, see how competitors add AI insights. To actually stay in the market, make AI the main driver of your product road maps. GCP Natural Language API accelerated our ability to adopt AI at scale. Thomas Squeo, CTO, NotifiedAcknowledgmentsWe’d like to thank our collaborators at Google and Notified for making this blog post possible. Thanks to Arpit Agrawal at MediaAgility for contributing to this blog post.To learn more about how Google Cloud Natural Language AI can help your enterprise, try out an interactive demo and take the next step, visit the product overview page here.Related ArticlePicture what the cloud can do: How the New York Times is using Google Cloud to find untold stories in millions of archived photosThe New York Times is building a pipeline on Google Cloud Platform to preserve its extensive photo archive, store it in the cloud, and le…Read Article
Quelle: Google Cloud Platform