Building real-time, interactive data products with open source data and analytics processing technology is not a trivial task. It involves constantly balancing cluster costs with service-level agreements (SLAs). Whether you are using Apache Hadoop and Spark to build a customer-facing web application or a real-time interactive dashboard for your product team, it’s extremely difficult to handle heavy spikes in traffic from a data and analytics perspective.
We’re pleased to announce Cloud Dataproc’s new autoscaling capabilities, now generally available, that can remove the need for complex capacity planning that always results in either missed SLAs or resources sitting idle.
How can autoscaling help your team? These new capabilities can help a range of teams, whether data engineers building complex ETL pipelines, data analysts running ad hoc SQL queries, or data scientists training a new model. Cloud Dataproc’s autoscaling capabilities allow cluster admins to build ephemeral or long-standing clusters in 90 seconds and apply an autoscaling policy to the cluster to minimize costs and maximize the user experience without manual intervention.
Whether you’re part of the team at a technology company building a SaaS application, a telecommunications company analyzing network traffic, or a retailer monitoring clickstream data during the holidays, you no longer have to worry about right-sizing clusters.
Right-sizing your cluster: Estimating the “right” number of cluster workers (nodes) for a workload is difficult, and a single cluster size for an entire pipeline is often not ideal. Don’t worry about manually right-sizing your cluster with autoscaling.
One autoscaling policy, multiple clusters: An autoscaling policy is a reusable configuration that describes how clusters using the autoscaling policy should scale. It defines scaling boundaries, frequency, and aggressiveness to provide fine-grained control over cluster resources throughout the cluster lifetime.
Budget optimization: Scale in and scale out clusters while setting limits in the autoscaling policy to make sure you don’t exceed budget.
YARN integration:Autoscaling policies integrate with YARN automatically to trigger VM scaling when needed, so you have one central resource management system for all of your Cloud Dataproc jobs.
Multi-region support: Deploy autoscaling clusters in any region where Cloud Dataproc clusters are running.
Check out our documentation to access everything you need to get started with Cloud Dataproc autoscaling. Autoscaling is supported through the v1 API on cluster image versions 1.0.99+, 1.1.90+, 1.2.22+, 1.3.0+, and 1.4.0+.
Being able to visualize data is often crucial to understanding it completely–and this is especially true for geo data. To help with this, Google Maps Platform enables you to add markers, lines, and shapes to a map programmatically. Depending on your purpose, whether you want to share or store the data, and the source of your data, working with specific formats can make the job easier. Today we’ll look at two different formats that you can easily add to Google Maps Platform projects for quick map layer visualizations: GeoJSON and GeoRSS.
GeoJSON, a JavaScript-friendly format We’ll start with the most flexible of the data formats, which can be called either inline or loaded from a URL. JavaScript developers may already be familiar with JavaScript Object Notation (JSON), a standard that is easily evaluated by JavaScript and other modern languages. GeoJSON uses JSON to encode geographic data structures and has become a popular way to share locations.
Here’s an example GeoJSON file with a single location:
This describes the location of an early capitol building: Congress Hall in Philadelphia, Pennsylvania.
Google Maps Platform supports GeoJSON data with a single function call. Other formats in this post will need to reference public URLs, but with GeoJSON we can reference the object right in our JavaScript–an external file is optional. Then we can reference it with the addGeoJson function.
Here’s our Google Maps JavaScript code, including the embedded GeoJSON object:
Alternatively, you can load an external file using loadGeoJson and include it in the map as a Data Layer. For more on this approach to GeoJSON, see the Data Layer documentation.
GeoRSS, derived from syndication format RSS You may want to add geographic data to an existing content feed. GeoRSS is the right choice, as it’s a data format designed to work with RSS feeds. For example, a travel blog could include geotagged places with every post, which could then be added to a map. The GeoRSS feed could also be standalone and not delivered with other content.
There are two versions: GeoRSS-simple and GeoRSS GML. GeoRSS-simple is designed to be really quick and straightforward, great for most Google Maps Platform use cases, and is the format you’ll probably use most often. But for more on GeoRSS GML, especially if working with different coordinate systems, see the GeoRSS site.
You can make a point with GeoRSS-simple like so:
40.70739, -74.0102
In this example, the data represents the location of another early US capitol building: Federal Hall in New York City.
GeoRSS files can be added to your Google Maps Platform projects with the same JavaScript function we used with KML. In this case, you’ll need to provide a publicly-accessible URL for a GeoRSS feed:
When you load this map, you’ll see markers read in from the Flickr GeoRSS file.
You can continue adding markers and other overlays to your Google maps using the built-in objects in the Maps JavaScript API. In situations where you have a lot of data at once, especially from an external source, it may make sense to use one of these common geographic data formats. Whether you like the portability of GeoJSON or the feed-interactivity of GeoRSS, you can easily add them as a visualization layer to your Google Maps.
One of our most important goals at Google Cloud is to make cloud computing easier, so that you can focus on answering questions that matter the most to you, your business and your users—not on managing infrastructure.
Today, we are excited to announce the preview of Batch on Google Kubernetes Engine (GKE), a cloud-native solution for running batch workloads at scale in an optimized manner. Batch on GKE brings the functionality and familiarity of a traditional batch job scheduler into a cloud-first world. It frees your applications from the limitations of fixed-sized compute clusters by dynamically allocating resources to meet the needs of your application.
Google Cloud is the home of Kubernetes—originally developed here and released as open source in 2014. GKE is a managed, production-ready environment for deploying containerized applications that relieves your teams from the operational toil of Kubernetes cluster management, allowing you to focus on your business needs. We heard you wanted to bring the benefits of GKE to batch workloads such as media rendering, genomics sequencing, silicon design verification and financial portfolio risk analysis, so we built Batch on GKE to bring what you love about GKE to these real-world batch workloads.
The preview release of Batch on GKE comes with the following capabilities:
Autoscaling and just-in-time provisioning to ensure you pay for just what you need
Rightsizing of virtual machines to tailor fit CPU and memory for the job at hand
Smart reuse of virtual machines and smart packing of jobs to reduce waste and the time jobs spend waiting in a queue
Resource budgets to allocate the maximum spend per team
Graphics Processing Unit (GPU) support
Job submission tool that high performance computing practitioners will find familiar
Batch on GKE’s easy-to-use, familiar interface enables you to deliver business results for your batch computing use cases. The following diagram shows how users can leverage Batch on GKE and other Google Cloud services to build a genomics processing and analysis system.
Meeting you where you are with partners Batch on GKE is a great solution for modernizing your batch workloads. We’re also committed to helping you migrate your existing systems as-is to Google Cloud or augment your on-premise setup by connecting to Google Cloud. We partner with SchedMD, Altair and Univa to integrate their market-leading schedulers with our platform and meet you where you are.
Engineering simulation made easy We’re also working closely with Rescale to enable their full-service HPC platform to run engineering simulations on Google Cloud and leverage on-demand GCP clusters and virtually unlimited cores. With this integration, you can run and manage simulations from a vast application library, including ANSYS Fluent, LS-DYNA, Star-CCM+ and more. Click here for a list of all the software that Rescale supports on Google Cloud.
This post was co-authored by Mikhail Shilkov, Software Engineer, Pulumi.
Pulumi is reinventing how people build modern cloud applications, with a unique platform that combines deep systems and infrastructure innovation with elegant programming models and developer tools.
We live in amazing times when people and businesses on different continents can interact at the speed of light. Numerous industries and applications target users around the globe: e-commerce websites, multiplayer online games, connected IoT devices, collaborative work and leisure experiences, and many more. All of these applications demand computing and data infrastructure in proximity to the end-customers to minimize latency and keep the user experience engaging. The modern cloud makes these scenarios possible.
Azure infrastructure
Azure Cosmos DB provides a turn-key data distribution to any number of regions, meaning that locations can be added or removed along the way while running production workloads. Azure takes care of data replication, resiliency, and efficiency while providing APIs for read and write operations with a latency of less than 10 milliseconds.
In contrast, compute services—virtual machines, container instances, Azure App Services, Azure Functions, and managed Azure Kubernetes Service—are located in a single Azure region. To make good use of the geographic redundancy of the database, users should deploy their application to each of the target regions.
Globally distributed application
Application regions must stay in sync with Azure Cosmos DB regions to enjoy low-latency benefits. Operational teams must manage the pool of applications and services to provide the correct locality in addition to auto-scaling configuration, efficient networking, security, and maintainability.
To help manage the complexity, the approach of infrastructure as code comes to the rescue.
Infrastructure as code
While the Azure portal is an excellent pane-of-glass for all Azure services, it shouldn’t be used directly to provision production applications. Instead, we should strive to describe the infrastructure in terms of a program which can be executed to create all the required cloud resources.
Traditionally, this could be achieved with an automation script, e.g., a PowerShell Cmdlet or a bash script calling the Azure CLI. However, this approach is laborious and error prone. Bringing an environment from its current state to the desired is often non-trivial. A failure in the middle of the script often requires manual intervention to repair environments, leading to downtime.
Desired state configuration is another style of infrastructure definition. A user describes the desired final state of infrastructure in a declarative manner, and the tooling takes care of bringing an environment from its current state to the parity with the desired state. Such a program is more natural to evolve and track changes.
Azure Resource Manager Templates is the bespoke desired-state-configuration tool in the world of Azure. The state is described as a JSON template, listing all the resources and properties. However, large JSON templates can be quite hard to write manually. They have a high learning curve and quickly become large, complex, verbose, and repetitive. Developers find themselves missing simple programming language possibilities like iterations or custom functions.
Pulumisolves this problem by using general-purpose programming languages to describe the desired state of cloud infrastructure. Using JavaScript, TypeScript, or Python reduces the amount of code many-fold, while bringing constructs like functions and components to the DevOps toolbox.
Global applications with Pulumi
To illustrate the point, we developed a TypeScript program to provision a distributed application in Azure.
The target scenario requires quite a few resources to distribute the application across multiple Azure regions, including:
Provision an Azure Cosmos DB account in multiple regions
Deploy a copy of the application layer to each of those regions
Connect each application to the Azure Cosmos DB local replica
Add a Traffic Manager to route user requests to the nearest application endpoint
Global application with Azure and Pulumi
However, instead of coding this manually, we can rely on Pulumi’s CosmosApp component as described in How To Build Globally Distributed Applications with Azure Cosmos DB and Pulumi. The component creates distributed Azure Cosmos DB resources, as well as the front-end routing component while allowing pluggable compute layer implementation.
Pulumi CLI executes the code, translate it to the tree of resources to create, and deploys all of them to Azure:
After the command succeeds, the application is up and running in three regions of my choice.
Next steps
Infrastructure as code is instrumental in enabling modern DevOps practices in the universe of global and scalable cloud applications.
Pulumi lets you use a general-purpose programming language to define infrastructure. It brings the best tools and practices from the software development world to the domain of infrastructure management.
Try the CosmosApp (available on GitHub—TypeScript, C#) with serverless functions, containers, or virtual machines to get started with Pulumi and Azure.
We are pleased to announce that Curie, a Google-owned subsea cable, has been successfully installed and tested. We are currently connecting it to Google’s network, and expect the private cable to begin transmitting data in Q2 2020, powering Google services like Gmail, Search, YouTube and Google Cloud.
Curie: A look inside Google’s International Private Subsea Cable
Equipped with four 18 Tbps fiber-optic pairs and running from the United States to Chile, Curie is 10,500 kilometers long and delivers 72 Tbps of much needed bandwidth to South America. Curie landed in Valparaiso last April, and was the first subsea cable to connect to Chile in 19 years. The historic landing was made possible in partnership with SubCom, a global partner for undersea data transport, which engineered, manufactured and installed the Curie system ahead of schedule.
We are excited to announce we’re adding the first Curie branch into Panama. Once operational, this branch will enhance connectivity and bandwidth to Central America, and increase our ability to connect to other networks in the region, providing resiliency to our global cloud infrastructure. For seamless integration, SubCom has also been selected to supply the Curie branch to Panama.
By owning and operating our own subsea cables we can add a layer of security beyond what’s available over the public internet, and can plan effectively for the future capacity needs of our customers and users around the world.
Commissioned in 2018, Curie was our third wholly-owned subsea cable. Since then, we’ve also commissioned Dunant, which crosses the Atlantic from the Virginia coast in the U.S. to the French coast; and Equiano, which will routes from Portugal to South Africa. Once again, we’re reminded that the cloud isn’t in the sky—it’s in the ocean.
We want to empower developers no matter where their businesses are in their cloud journey, whether that’s on-prem, operating in a managed Kubernetes environment, or running on a fully managed serverless computing platform. Today, we’re announcing that Cloud Run is generally available, helping developers focus on writing high-value code, regardless of where their organizations are on the path to the cloud. Specifically, we’re announcing:
Cloud Run, a fully managed serverless execution environment that lets you run stateless HTTP-driven containers, without worrying about the infrastructure.
Cloud Run for Anthos, which lets you deploy Cloud Run applications into an Anthos GKE cluster running on-prem or in Google Cloud.
Our commitment to Knative, the open API and runtime environment on which Cloud Run is based, bringing workload portability and the serverless developer experience to your Kubernetes clusters, wherever they may be.
Cloud Run fully managed
Cloud Run brings the best of both serverless and containers together. It allows you to write code in any language you choose, using any binary, without having to worry about managing the underlying infrastructure. Cloud Run offers a natively serverless experience that lets you go from container to URL within seconds, for terrific developer velocity. You’re also charged only for the resources used, billed to the nearest 100 milliseconds. And Cloud Run workloads are totally portable: you can run them fully managed on Google Cloud, on Anthos running on-premises, or Anthos running on Google Cloud, or on a third-party cloud platform that supports Knative.
Customers such as Narvar, a post-purchase platform for retailers, are already leveraging the benefits of fully managed Cloud Run.
“Google Cloud Run accelerated our developer velocity by 3X by enabling our developers to create new services at scale with ZERO dev-ops involvement. Just as important, Cloud Run helped us create a net-new product and business model, previously not possible because of the constraints of other solutions.” – Ram Ravichandran, CTO Narvar
As a managed compute platform, Cloud Run is production-ready out of the box with monitoring and logging powered by Stackdriver, all integrated into Cloud Console. Cloud Run also has first-class integration with gcloud, GCP’s CLI experience, giving you a consistent interface for deploying and managing Cloud Run services across these platforms.
Cloud Run for Anthos
If your organization wants to leverage Kubernetes to modernize and optimize its existing resources, the combination of Anthos and Cloud Run for Anthos can help smooth that transition, so you can modernize in place. With Cloud Run for Anthos, developers can easily write serverless applications and deploy them to the Anthos cluster without having to learn Kubernetes concepts first. Cloud Run for Anthos takes care of scaling your application instances up or down, even down to zero depending on traffic.
Cloud Run for Anthos also lets you future-proof your applications. If you want to modernize an on-prem legacy application in place or extend it to the cloud, Cloud Run for Anthos can simply spin up a microservice that talks privately to existing services—all running on the same Anthos cluster. And if you decide you want to move your Cloud Run for Anthos apps to the cloud, it’s just a simple redeploy to the fully managed Cloud Run. You can even choose to move your workloads back to your own datacenter or another third-party cloud running a Knative-compatible environment. Here is what one of our customers, QiTech had to say:
“Cloud Run for Anthos brings additional features like application level auto scaling to our Kubernetes clusters, making it very simple to manage the cluster to scale when needed. It takes various actions that follow Kubernetes best practices and abstracts them, allowing us to focus more on our service and less on its infrastructure.” – Danilo Porto, Lead DevOps Engineer, QI Tech
We’ve also been collaborating with popular ISV partners to extend the capabilities of Cloud Run for Anthos across key areas like CI/CD, application performance monitoring (APM), and security. We’re excited to announce support from CircleCI, CloudBees, Datadog, Dynatrace, GitLab, JFrog, New Relic, Octarine, Palo Alto Networks, PureSec, Sumo Logic, and Sysdig. These partnerships mean organizations can easily integrate Cloud Run for Anthos using popular ISV solutions and still take advantage of existing tooling investments.
Cloud Run is possible, in part, through our longstanding commitment to open source. We started Knative more than a year ago to help developers easily write serverless applications on top of Kubernetes. Working alongside industry leaders such as IBM, Pivotal, Red Hat, SAP and TriggerMesh, we built Knative to provide the essential components you need to build, deploy and manage modern serverless workloads anywhere you choose.
The Knative project is supported by more than 100 organizations and approximately 450 individual contributors. Already, organizations such as T-Mobile are building innovative applications for production use cases with Knative. Releases over the last year have focused on codifying best practices from successful real-world Kubernetes frameworks, and collecting feedback to ensure Knative delivers on its promise of portability and ease of use. By bringing these learnings to Cloud Run, we aim to bring the serverless developer experience to your Kubernetes cluster anywhere.
Cloud Run, don’t walk, to serverless containers
Here at Google, we believe in the power of serverless and the benefits of containers, and give you the best of both with Cloud Run. We also believe that the tools to enable serverless containers should be built in the open, by the community. Check out our serverless quest to give Cloud Run a try. If you’re already using Cloud Run for Anthos, please upgrade your GKE cluster. It’s your journey—we’re excited to be there alongside you.
It’s undeniable that technology has improved many facets of modern life. Transportation and mobility, however, continues to be an area where more can be done. For example, look no further than your daily commute. Research by INRIX shows that time spent in traffic has more than doubled in many major cities around the world since 2015.
But five megatrends hold the promise of bringing modern technology’s full benefits to the mobility sector, to improve the commuter experience and more: autonomous driving, shared mobility, deeper customer insights, digital manufacturing, and connected cars. These trends have the potential to fundamentally change the way the automotive industry works, and cloud technology will play a major role in getting there.
Let’s look at each of these trends and see how we’re helping drive them.
Autonomous driving
Given its impact on both technology and fundamental business models, autonomous driving is perhaps one of the most dramatic revolutions in mobility. Building the infrastructure and providing the tools for companies to build autonomous driving solutions is an area where the cloud can provide great value. At the same time, building an artificial intelligence (AI) system that can be a safe driver is one of the most demanding machine learning (ML) problems to solve at industrial scale.
To validate autonomous driving models, companies need to test cars both on the road and in complex digital simulations. These simulations involve massive computational demands and volumes of data, and are often best served with a combination of GPUs and Cloud TPUs (Tensor Processing Units). We custom-built Cloud TPU for AI workloads, and its high-speed network offers over 100 petaflops of performance in a single pod—essentially making it an on-demand supercomputer. So, no matter what your workload is, we have the world-class AI infrastructure to run it as efficiently and inexpensively as possible.
At the driver level, developers can embed a Google Maps-powered turn-by-turn navigation experience into their applications. This means drivers don’t have to switch between apps to get directions or information about their next job. It also lets companies retrieve data about a driver’s journey. With this programmatic control and insight into drivers’ behavior, organizations can better allocate drivers, ultimately decreasing drivers’ idle time and customer wait times. One early user of our ridesharing solution reported a 4% reduction in drive times and up to a 48% increase in the accuracy of estimated times of arrival.
Gaining deeper customer insights
While automotive manufacturers create some of the most iconic consumer products, gaining customer insights can be very difficult. That’s because customer-related data is often fragmented; some resides at dealerships, while some is with the manufacturer itself—potentially distributed over many non-connected systems.
Siloed data can lead to a number of inefficiencies, especially around automotive incentive spends. According to McKinsey, these incentives are among the largest expenses that car companies have, but are the least understood and managed. Additionally, discounts and rebates introduce enormous variation and complexity for dealers and original equipment manufacturers (OEMs) around customer money, dealer money, lease discounts, bonuses for members of the National Funeral Directors Association, and rebates for realtors, as a few examples.
This complexity can result in real pricing confusion. In a recent study, Cox Automotive’s rates and incentives unit compared interest rates, cash, and incentives through dealer service provider tools and found pricing fluctuations of up to $6,750. And one thing consumers don’t like is uncertainty. J.D. Power’s 2018 U.S. Sales Satisfaction Index shows that 14% of customers who shopped but didn’t buy a vehicle at a dealership said they rejected the store because they had difficulty getting a straight answer on price.
With Google Cloud, automotive incentives can be optimized in a way that helps OEMs and dealers control critical costs and eliminate confusion. Using BigQuery, they can ingest, store, and analyze data that can connect the dots between the OEM, dealer, and customer. This process enables incentives to be tracked accurately and consistently. In addition, Google Cloud’s advanced AI/ML tools can investigate patterns in past rebates, so OEMs and dealers can learn whether they’re applying rebates effectively, and even provide automatic triggers to point out potential overspending due to factors like overlapping/duplicative incentives and conflicting rebates.
To pull it all together, Google Cloud APIs let dealers connect to their dealer management systems (DMS) for “one source of truth” for data across their systems-of-record. Finally, Google Cloud has a network of systems integrators and other consulting partners in the automotive industry who can help implement these incentive optimization solutions.
Digital manufacturing
Despite improvements in robotics, AI, and other digital technologies, the automotive manufacturing shop floor remains mired in decades-old systems and siloed data. IDC notes that 77% of manufacturers view digital transformation as an opportunity, making it one of their top priorities. And McKinsey says the new era of automated production and data exchange opens a broad range of use cases that can cut costs, increase yields, and support new manufacturing methods.
Automotive companies, however, often want to run manufacturing workloads on premises. This could be for a number of reasons, such as latency, data residency requirements in some countries, and customer preferences to keep data local. Anthos, our managed, cloud-native platform, addresses these challenges, while still allowing customers to develop and operate their systems as if they were in the cloud. As a software-only stack, Anthos runs on customers’ choice of hardware. Moreover, Anthos workloads can seamlessly move to Google Cloud, or other cloud vendors, at any time.
Quality inspection is another area where our customers are seeing tangible operational improvements with the help of cloud and machine learning technologies. Edge TPU—our ASIC (application-specific integrated circuit) designed to run at the edge with a small power and physical footprint—can let manufacturers run inspections on the shopfloor. And outside the auto industry, LG CNS is already using our technology to detect defects in LCD panel production.
Connected Cars
Today’s modern automobile is a supercomputer that generates enormous amounts of data—data that’s captured from as many as 60-100 sensors, and often in real-time. Unfortunately, much of this data is unstructured, in siloed systems, and vulnerable to hacks. At the same time, the electronics and software inside the vehicle are getting exponentially more complex. Between 2010 and 2016, the lines of code required for an average vehicle increased 15-fold, while the complexity of suppliers and processes has also multiplied.
We want to make automotive software simpler and help companies draw insights from the data their fleets generate. We’ve already taken a big step with Android, which has seen significant adoption among automotive OEMs. With the cloud, we can offer a fully integrated approach to telemetry, making it much easier to extract and run analytics on vehicle data.
Of course, whenever data is moving between organizations, trust and security are of utmost importance. That’s why, with our method, all businesses, including OEMs, retain full control and ownership of their data. We also build our own chips to ensure the integrity of our data centers and your data.
Transportation and mobility is a complex field, and its digital transformation has the potential to touch almost every single one of us in some way. Our solutions aim to help automotive companies continue to make that transition, and we look forward to seeing the innovative ways they use cloud technology to reach their goals.
Contact centers are one of the most important ways businesses respond to and meet customer needs. However, the demand for quality customer service comes with its own set of operational challenges. As a result, many businesses are embracing cloud technologies and AI to help provide better and more helpful customer experiences, more efficiently.
Contact Center AI, which we announced last July, lets you provide personalized, intuitive customer care from the first “Hello.” Now, we’re pleased to announce that businesses can easily start implementing Contact Center AI today to drive results that matter to your customers and your business.
Contact Center AI is now GA, and ready to transform contact centers
Virtual Agent and Agent Assist, two features of Contact Center AI that are now both GA, work together to achieve a common goal: improve the customer experience while increasing operational efficiency. Here’s how they do it:
Virtual Agent: Customers now have 24/7 access to immediate and personalized conversational self-service, as Virtual Agent automates basic interactions and provides seamless handoffs to human agents for more complex issues through real-time call transcription.
Agent Assist: Live agents are empowered with continuous support during their calls, as Agent Assist transcribes calls in real time, identifies customer intent, provides real-time, step by step assistance (recommended articles, workflows, etc.), and automates call dispositions.
You can now integrate Contact Center AI with your existing workflows and start seeing results within 3-6 months, thanks to integrations with partners such as Avaya and Mitel, who are GA today, as well as 8×8, Cisco, Five9, Genesys, Salesforce, Twilio, and Vonage.
Create an even better conversational experience with key Dialogflow updates
At the heart of Contact Center AI is Dialogflow, our core technology that builds interfaces such as chatbot and interactive voice responses (IVR), to enable natural and rich conversational experiences.
Today, Dialogflow has reached over 1 million developers, supports HIPAA and PCI compliance, and has enhanced support for Virtual Agents:
Self-service Dialogflow Telephony Integrations: Helps set up your virtual agent faster, as we’ve expanded Dialogflow Telephony Gateway to include IVR partners. Through partner instructions, you can use existing phone numbers to access your Dialogflow virtual agent and power your IVR systems in no time.
Agent validation: Helps agent designers create high-quality virtual agents to improve the overall experience, by providing feedback with a list of errors or warnings that should be fixed to improve the quality and performance of the virtual agent.
Entity enhancements: Through these features, we have improved our natural language processing to provide an even more accurate and natural conversational experience.
System entity extension: Offers greater accuracy and recognition, by adding additional values to your Dialogflow application to extend system entities.
Regular expression entities: Improves return matches through regular expression entities, matching patterns instead of specific terms (examples include: national identification numbers, IDs, license plates, etc.).
Fuzzy matching: Provides better entity matching, regardless of word ordering in a value or synonym, to make it easier for developers to create entities.
Start seeing results in your Contact Center today
Customers have started seeing immediate results from Contact Center AI integrations. Here’s what our customers have to say:
“To deliver on a best-in-class viewer experience, we need to respond effectively to our viewers whenever they’re reaching out to Hulu for help,” says Matt Kravitz, Director of Viewer Experience at Hulu. “Because of this, we’re always searching for innovative technology that will help our customer support advocates respond and react quickly. Contact Center AI from Google Cloud and Salesforce was the best solution for our needs as it enables recommended responses and next best actions. With these tools, our team can focus more on engaging with our viewers.”
“GoDaddy uses Dialogflow, a core component of Contact Center AI, to power our conversational self-help experience. It furthers our ability to provide exceptional care and guidance to our customers, both digitally and while interacting with our human Guides,” says Mira Lynn, Head of Conversational AI at GoDaddy. “We chose Dialogflow because of the promise of flexibility the platform provides. It has enabled us to develop a unique approach that maintains conversation designers’ independence from engineering development cycles, allowing us to move quickly and flexibly to iterate and respond to customer needs. Our agility has allowed us to provide a customer and Guide-first experience, giving our customers the right answers in the mode that they choose, while also enabling our Guides to better serve them.”
To find out how Contact Center AI can increase CSAT, deflection rates, and operational efficiency, visit our site, contact your Google sales representative, or request to be contacted.
Guest post by Nsubuga Hassan, CEO at Hansu Mobile and Intelligent Innovations, Android and Machine Learning DeveloperIn 2016 Fall armyworm (FAW) was first reported in Africa. It has devastated maize crops across the continent.
Research shows the potential impact of FAW on continental wide maize yield lies between 8.3 and 20.6 million tonnes per year (total expected production of 39m tonnes per year); with losses lying between US$2,48m and US$6,19m per year (of a US$11,59m annual expected value). The impact of FAW is far reaching, and is now reported in many countries around the world.
Agriculture is the backbone of Uganda’s economy, employing 70% of the population. It contributes to half of Uganda’s export earnings and a quarter of the country’s gross domestic product (GDP). Fall armyworm posses a great threat on our livelihoods. We are a small group of like minded developers living and working in Uganda. Most of our relatives grow maize so the impact of the worm was very close to home. We really felt like we needed to do something about it. The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help. It is the perfect time to apply machine learning. Our goal is to build an intelligent agent to help local farmers fight this pest in order to increase our food security.
Based on a Machine Learning Crash Course, our Google Developer Group (GDG) in Mbale hosted some study jams in May 2018, alongside several other code labs. This is where we first got hands-on experience using TensorFlow, from which the foundations were laid for the Farmers Companion app. Finally, we felt as though an intelligent solution to help farmers had been conceived.
Equipped with this knowledge & belief, the team embarked on collecting training data from nearby fields. This was done using a smartphone to take images, with the help of some GDG Mbale members. With farmers miles from town, and many fields inaccessible by road (not to mention the floods), this was not as simple as we had first hoped. To inhibit us further, our smartphones were (and still are) the only hard drives we had, thus decreasing the number of images & data we can capture in a day.
But we persisted! Once gathered, the images were sorted, one at a time, and categorized. With TensorFlow we re-trained a MobileNet, a technique known as transfer learning. We then used the TensorFlow Converter to generate a TensorFlow Lite FlatButter file which we deployed in an Android app. We started with about 3956 images, but our dataset is growing exponentially. We are actively collecting more and more data to improve our model’s accuracy. The improvements in TensorFlow, with Keras high level APIs, has really made our approach to deep learning easy and enjoyable and we are now experimenting with TensorFlow 2.0.
The app is simple for the user. Once installed, the user focuses the camera through the app, on a maize crop. Then an image frame is picked and, using TensorFlow Lite, the image frame is analysed to look for Fall armyworm damage. Depending on the results from this phase, a suggestion of a possible solution is given.
The app is available for download and it is constantly undergoing updates, as we push for local farmers to adapt and use it. We strive to ensure a world with #ZeroHunger and believe technology can do a lot to help us achieve this.
We have so far been featured on a national TV station in Uganda, participated in the #hackAgainstHunger and ‘The International Symposium on Agricultural Innovations’ for family farmers, organized by the Food Agricultural Organization of the United Nations, where our solution was highlighted.
More recently, Google highlighted our work with this film:
We have embarked on scaling the solution to coffee disease and cassava diseases and will slowly be moving on to more. We have also introduced virtual reality to help farmers showcase good farming practices and various training.
Our plan is to collect more data and to scale the solution to handle more pests and diseases. We are also shifting to cloud services and Firebase to improve and serve our model better despite the lack of resources. With improved hardware and greater localised understanding, there’s huge scope for Machine Learning to make a difference in the fight against hunger.
The Go gopher was created by renowned illustrator Renee French. This image is adapted from a drawing by Egon Elbre.
November 10 marked Go’s 10th anniversary—a milestone that we are lucky enough to celebrate with our global developer community.
The Gopher community will be celebrating Go’s 10th anniversary at conferences such as Gopherpalooza in Mountain View and KubeCon in San Diego, and dozens of meetups around the world.
In recognition of this milestone, we’re taking a moment to reflect on the tremendous growth and progress Go (also known as golang) has made: from its creation at Google and open sourcing, to many early adopters and enthusiasts, to the global enterprises that now rely on Go everyday for critical workloads.
New to Go?
Go is an open-source programming language designed to help developers build fast, reliable, and efficient software at scale. It was created at Google and is now supported by over 2100 contributors, primarily from the open-source community. Go is syntactically similar to C, but with the added benefits of memory safety, garbage collection, structural typing, and CSP-style concurrency.
Most importantly, Go was purposefully designed to improve productivity for multicore, networked machines and large codebases—allowing programmers to rapidly scale both software development and deployment.
Millions of Gophers!
Today, Go has more than a million users worldwide, ranging across industries, experience, and engineering disciplines. Go’s simple and expressive syntax, ease-of-use, formatting, and speed have helped it become one of the fastest growing languages—with a thriving open source community.
As Go’s use has grown, more and more foundational services have been built with it. Popular open source applications built on Go include Docker, Hugo, Kubernetes. Google’s hybrid cloud platform, Anthos, is also built with Go.
One exciting example of Go in action is at MercadoLibre, which uses Go to scale and modernize its ecommerce ecosystem, improve cost-efficiencies, and system response times.
MercadoLibre’s core API team builds and maintains the largest APIs at the center of the company’s microservices solutions. Historically, much of the company’s stack was based on Grails and Groovy backed by relational databases. However this big framework with multiple layers was soon found encountering scalability issues.
Converting that legacy architecture to Go as a new, very thin framework for building APIs streamlined those intermediate layers and yielded great performance benefits. For example, one large Go service is now able to run 70,000 requests per machine with just 20 MB of RAM.
“Go was just marvelous for us,” explains Eric Kohan, Software Engineering Manager at MercadoLibre. “It’s very powerful and very easy to learn, and with backend infrastructure has been great for us in terms of scalability.”
Using Go allowed MercadoLibre to cut the number of servers they use for this service to one-eighth the original number (from 32 servers down to four), plus each server can operate with less power (originally four CPU cores, now down to two CPU cores). With Go, the company obviated 88 percent of their servers and cut CPU on the remaining ones in half—producing a tremendous cost-savings.
With Go, MercadoLibre’s build times are three times (3x) faster and their test suite runs an amazing 24 times faster. This means the company’s developers can make a change, then build and test that change much faster than they could before.
Today, roughly half of Mercadolibre’s traffic is handled by Go applications.
“We really see eye-to-eye with the larger philosophy of the language,” Kohan explains. “We love Go’s simplicity, and we find that having its very explicit error handling has been a gain for developers because it results in safer, more stable code in production.”
Visit go.dev to Learn More
We’re thrilled by how the Go community continues to grow, through developer usage, enterprise adoption, package contribution, and in many other ways.
Building off of that growth, we’re excited to announce go.dev, a new hub for Go developers.
There you’ll find centralized information for Go packages and modules, a wealth of learning resources to get started with the language, and examples of critical use cases and case studies of companies using Go.
MercadoLibre’s recent experience is just one example of how Go is being used to build fast, reliable, and efficient software at scale.
You can read more about MercadoLibre’s success with Go in the full case study.
We are very excited to introduce Network Intelligence Center, Google Cloud’s comprehensive network monitoring, verification, and optimization platform across the cloud and on-prem data centers, along with an initial set of modules.
Organizations are increasingly adopting multi-cloud implementations and hybrid deployments as a part of their cloud strategy, to deliver the best experiences for their customers around the globe. Networks are at the foundation of this digital transformation, and becoming increasingly complex due to the growing heterogeneity in the customer environment. This puts pressure on network operations teams, who live under the presumption that “the network is guilty until proven innocent.” Unfortunately, today’s fragmented networking tools don’t make their lives any easier, leading to a lack of visibility, long troubleshooting times to resolve connectivity and performance issues, configuration errors being discovered too late in production, and no centralized way to holistically understand and remedy network health.
*1, *2 & **3
The vision for intelligent and predictive network operations
Adoption of hybrid and multi-cloud is absolutely critical for organizations to remain agile. However, this underscores the need for intelligent and continuous network operations—the promise that the network is doing what it needs to do, in line with business intent. For example, if you have global operations, you may want to ensure that users in a specific geographical region are being served out of the nearest datacenter at all times.
To realize this vision, you need proactive network operations that can predict and heal network failures, driven by AI/ML recommendations and remediation. These systems also need to maintain an equilibrium between automation and control, to support customers who are at different points in their journey from reactive towards proactive network operations.
In the first phase of fulfilling this vision for intelligent network operations, Network Intelligence Center offers four modules: Connectivity Tests and Network Topology, both in beta; Performance Dashboard and Firewall Metrics & Insights in alpha; with several other modules to follow.
Network Topology: Visualize complex networks with insights Google Cloud Platform (GCP) is the first cloud provider to provide comprehensive visibility into your global GCP deployment (not just your single VPC) and its interaction with the public internet, including an organization-wide view of the topology, and associated network performance metrics with Network Topology.
Visibility is especially important in cloud to gain trust and confidence since customers do not own the infrastructure. Visualizing cloud topologies is also complicated by the fact that a cloud network is really a mesh, making it hard to understand and navigate the differences between on-premises and cloud environments. Network Topology lets you visualize and navigate your network with structure and insights, simplifying network monitoring and troubleshooting, and helping you fulfill your compliance requirements.
Further, Network Topology provides useful performance metrics for various nodes and edges, enabling metrics-driven troubleshooting, policy checks, and architecture and capacity optimization. For instance, you can easily visualize how your users are being served worldwide and if they are being served optimally out of their nearest geographical regions. You can quickly search through complex networks and drill down into exactly what matters to you, shrinking troubleshooting time. In the event of a network issue, you can go back in time up to six weeks to track the evolution of the network’s topology, to diagnose issues quickly.
“Network Intelligence Center has been transformational for us to optimize our network operations. Using Network Intelligence Center we discovered that data transferred to a particular GCE region was significantly higher than expected. By looking at Network Topology we were able to diagnose and fix this issue, and save significant costs,” said Rob Lyon, Enterprise Architect at Kochava, a mobile app analytics company.
“Network Intelligence Center helps us quickly identify and troubleshoot network problems. We’ve gained full situational awareness of our network topology, saving us time on monitoring network health,” said Jason Cradit, Senior Director of Technology at Pivvot, a real-time location data and analytics company.
Visualize your network with insights: See metrics for nodes and edges.
Egress traffic breakdown from one region (europe-west1 here) to other regions can be used for optimization
Connectivity Tests: Diagnose connectivity issues and predict the impact of configuration changes GCP is the first cloud provider to provide a solution based on formal verification techniques that empowers you to diagnose connectivity issues, and proactively verify the impact of configuration changes to prevent outages.
75% of network outages and performance issues result from misconfiguration. More often than not, these misconfigurations are discovered in production. Not knowing the impact of making a configuration change in firewall rules or routing rules makes network monitoring reactive rather than proactive, introducing risk and extending mean time to resolution.
Network Intelligence Center helps to quickly diagnose connectivity issues and prevent outages by allowing teams to perform on-demand tests with the Connectivity Test module. Connectivity Test enables you to self-diagnose connectivity issues within GCP or GCP to an external IP address which could be on-prem or another cloud, helping to isolate whether the issue is in GCP or not. You can create, save and run tests to help verify the impact of configuration changes and ensure that network intent captured by these tests is not violated, proactively preventing network outages. These tests also help assure network security and compliance. Connectivity Test has been used internally by Google Cloud’s support team to resolve customer issues.
“We’re excited that with the help of Network Intelligence Center, we’ll be able to verify that our network connectivity matches intent and quickly troubleshoot network configuration issues,” said David Breise, Cloud & Network Engineer at HIPAAVault, a secure HIPAA-compliant file-sharing provider.
Create & save connectivity tests that reflect your intent. Any time you make configuration change, run them to verify impact.
Speed up cloud migrations
In addition to monitoring, verification, and optimization of the network between on-premises and cloud, Network Intelligence Center can also help accelerate your migration to cloud. With a holistic view into network architecture, health and performance before and after migration, it allows you to plan your cloud migrations efficiently and optimize your architecture. It also helps to identify and debug network issues between on-prem and cloud, and can help you easily track topology changes during migration, as well as view actual traffic flows and performance metrics before and during the migration.
Additional modules: Performance Dashboard and Firewall Metrics & Insights
Network Intelligence Center includes two additional modules in alpha: Performance Dashboard and Firewall Metrics & Insights.
Performance Dashboard provides real-time network performance visibility into packet loss and latency at a per-project level, a compelling capability for network health monitoring at a project level that reflects a user’s network experience, not offered by any other cloud provider today. These metrics provide insights into your network performance, showing VM-to-VM packet loss and latency data aggregated across zones. In the case of an application performance issue, Performance Dashboard helps the network team quickly determine whether the issue is with the network or with the application. Fill out this form to sign up as an alpha customer for Performance Dashboard.
Firewall Metrics & Insights provides visibility into firewall rules usage, helping you optimize firewall rules by revealing insights into shadowed firewall rules. You can access this capability today from the firewall rules page in alpha, and it will be integrated into Network Intelligence Center when it reaches beta. Fill out this form to sign up as an alpha customer for Firewall Metrics & Insights.
Moving from reactive to proactive network operations
As you build more and more applications on a Google Cloud foundation, you need a network operations solution that lets you easily visualize complex, cloud-based topologies, helps you proactively identify and troubleshoot problems, and assists you in making good decisions when running at global scale. We are excited to see you use Network Intelligence Center to adopt a more proactive approach to monitoring network health and to predict, diagnose and verify your network quickly. Stay tuned for new Networking Intelligence Center modules as well as blogs that dive deep into Connectivity Test and Network Topology. Meanwhile, give Connectivity Test and Network Topology a try in the Google Cloud Console. Also, visit our website to learn more about networking and take a tour of Cloud City. And as always, reach out to the Google Cloud Networking team with your questions and feedback.
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In 2016 
We are a small group of like minded developers living and working in Uganda. Most of our relatives grow maize so the impact of the worm was very close to home. We really felt like we needed to do something about it. 
The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help. It is the perfect time to apply machine learning. Our goal is to build an intelligent agent to help local farmers fight this pest in order to increase our food security.
We started with about 3956 images, but our dataset is growing exponentially. We are actively collecting more and more data to improve our model’s accuracy. The improvements in TensorFlow, with Keras high level APIs, has really made our approach to deep learning easy and enjoyable and we are now experimenting with 
Posted by Steve Francia, Go Team
Posted by Soc Sieng, Developer Advocate
