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In this year’s NEXT session: AI103 Using CCAI insights to better understand your customers, a new conversational AI tool has been introduced, CCAI Insights. With Contact Center AI Insights, business stakeholders and QA compliance teams can analyze and monitor customer service interactions and patterns in their contact center data. It gives businesses insights into the topics that are being discussed by their end-users. You can monitor how those conversations have been handled by the service agent through transcripts, caller sentiment detection, silence detection, entity identification, and topic modeling.CCAI Insights can be used stand-alone but it also seamlessly integrates with all other Contact Center AI Solution products like Dialogflow and Agent Assist, as part of our Conversational AI offerings.The first thing you will have to do is import conversations to your CCAI Insights instance. In a production environment, you will likely have CCAI Insights integrated with your virtual agent and contact center systems that push conversations via the runtime integrations in real-time to CCAI Insights. – However, it’s also possible to import existing datasets manually.Importing a text chat conversationLet’s start with importing a text conversation between an end-user and a virtual agent into CCAI insights. The data that’s imported in CCAI insights, under the hood makes use of Cloud’s Spanner. In case regionalization matters to you, because of enterprise data regulations, it’s good to know that US and EU regionalization is on the roadmap for early next year. With that being said, there is also a setting to delete the data after a preset period of time (TTL) and all data can be exported via API, Cloud Data Fusion, or direct to BigQuery.You can import it through Google Cloud Storage by pointing to the GCS URL and providing the name of the virtual agent who handled the chat.As seen in the listing below. Your conversation will need a specific JSON format, which defines: the text, the timestamp, the user id, and the role.Once the conversation is imported, you can dive into the conversation and press the Start analysis button. This will analyze your conversation transcript and annotate bits of your conversation, such as locations, persons, or objects. Clicking on these entities will highlight the parts of the conversation where those entities were mentioned. You can imagine that it’s extremely useful for a business or contact center managers to get insights on the topics that are being discussed in the call or chat. For example, in the case of a chatbot, are these the topics the chatbot was trained on? Or should you come up with a set of intents?In the conversation hub, you can use the filter to include or exclude conversations based on agent ID, transcript, duration, turn count, and more. These filters can be combined to find specific conversations, and it’s possible to label these so you can find it back, or if you want to review these over a longer period of time.Importing a call (audio) conversationWe can do the same for audio recordings. You will need to have a two-channel audio file of a uniform sample rate and encoding supported by Cloud Speech-to-Text. Speech-to-Text could generate a transcript from an audio file. What’s important is that your transcript matches the Speech-to-Text response format, which contains the bits of a sentence with the start and end timestamps for each word, as shown in the below listing. Each conversational turn is tagged with a channel tag to refer to the speaker that is speaking on that channel.Once you dive into your conversation, you can analyze the audio, and it’s also possible to play the audio recording.Besides the entities, chat and audio conversations can also analyze the silence and the sentiment of the caller and the agent. This is very useful for contact center managers who want to learn from customer escalations. Importing large datasetsLastly, you can also import conversations as a batch to import existing large datasets. You can import these through scripts using the API or via Cloud Data Fusion.Topic modelingA CCAI Insights topic model uses Google’s Natural Language Processing to generate primary topics for each conversation in your dataset. You can then deploy the model to analyze future conversations as they’re imported.To train your own topic model with good accuracy, you will need a minimum of 10 thousand conversations. Then you can start the training. Please understand that training a topic model can take up to 12 hours, as it’s a very extensive process, as it analyzes every conversation with each other, to find the most common entities.Once your model has been successfully trained from customer data, you can deploy the model and view the most used topic drivers. Note the screenshot below, who would have known that apparently your human or virtual service agents spend a lot of time answering questions on how people can login to their accounts!Conversation highlightsSmart Highlights automatically detect highlights through keywords and/or phrases in your conversation without requiring additional configuration. Smart Highlights draws from various possible scenarios to detect highlights, such as asking to hold, ensuring that an issue was resolved, a complaint, and more. Any highlights present in a conversation are labeled in the returned transcript on sentence level. It analyzes each conversation turn and categorizes the user’s intention.It’s also possible to create your own highlighters by providing keywords. In the below screenshot, you can see a custom highlighter tagging conversational turns discussing money amounts.When using CCAI Insights combined with Dialogflow, it’s possible to create intelligent highlighters using Dialogflow intents.As you have read in this article, CCAI Insights enables businesses to hear what customers are saying to make data-driven business decisions and increase operational efficiency. To learn more about CCAI Insights, check out the documentation.Related ArticleGoogle Cloud expands CCAI and DocAI solutions to accelerate time to valueGoogle Cloud deepens customer understanding with Contact Center AI Insights and transforms contract management with Contract DocAIRead Article
Quelle: Google Cloud Platform

15. Oktober 2021

da Agency

Next Reaction: Features to reduce IT carbon emissions and build climate-related solutions

Climate technology strategies are becoming increasingly important. A Google-commissioned study by IDG shows that 90% of IT departments are making sustainability a priority; and with the advances in machine learning, many organizations are also increasingly interested in building climate solutions whether it’s using geo-spatial data or predictive maintenance. This is why we are happy to share four announcements at NEXT 2021 that help IT teams improve their sustainability efforts, or build complex ML big data climate solutions that are usually computationally intensive. From a making IT operations greener, Google Cloud is offering two fundamental tools:1) A free Carbon Footprint dashboard is now available in your Cloud project. It displays gross carbon emissions from the electricity associated with the usage of covered Google Cloud services for the selected billing account. With growing requirements for Environmental Social and Governance (ESG) accuracy; accounting for IT carbon emissions is necessary to measure progress against the carbon reduction targets required to avert the worst consequences of climate change. By using Carbon Footprint, you have access to your cloud infrastructure’s energy related emissions data needed for your internal carbon inventories and external carbon disclosures, with one click. This dashboard was built in collaboration with customers like Atos, Etsy, HSBC, L’Oréal, and Salesforce.Ensure you have viewer access to the billing account. You will be able to view pre-built charts that summarize kilograms of CO2 equivalent (kgCO2e) in the past 12 months, by project, product, and region. It can take up to 21 days for the previous month to become available.For greater customization, you can optionally export your Carbon Footprint data to BigQuery which is a database with a free tier of upto 10GB of storage and 1 TB of queries per month in order to perform data analysis, or create custom dashboards and reports. You can optionally further dig into the carbon footprint reporting methodology. 2) We are helping IT practitioners make informed decisions when selecting the greenest compute resources. When selecting a region you can view the lowest carbon impact inside Cloud Console location selectors with the green leaf icon.Drop-down with green leaf icons to select a less carbon emitting region. The following tool also enables you to make greener choices when choosing what regions to house your compute resources, and accounts for variables like price, latency, and sustainability.Screenshot of Google Cloud region picker to help customers select the greenest regions for their cloud projects.One of the greatest IT low-hanging fruits for reducing gross emissions is locating and deleting unattended projects using the Active Assist Recommender. The recommender uses machine learning to identify, with a high degree of confidence, projects that are likely abandoned based on API and networking activity, billing, usage of cloud services, and other signals. By deleting these projects, you reduce costs, mitigate security risks, and reduce carbon emissions.In August, Active Assist analyzed the aggregate data from all customers across our platform, and over 600,000 kgCo2e was associated with projects that it recommended for cleanup or reclamation. If customers deleted these projects they would significantly reduce future emissions.The next two announcements help organizations build climate solutions using ML and satellite imagery:1) Whether your organization is trying to understand changes on the Earth due to supply chain operations, or performing risk modeling on upcoming climatic changes, developers and scientists have turned to Google Earth Engine because it houses the world’s largest catalog of satellite imagery and geo-spatial data. Over the past year we have worked with numerous organizations that use Earth Engine datasets and analyze them in managed services like a BigQuery database or apply Machine Learning via Vertex AI (to name a few). This is why we are offering an enterprise-grade experience of Earth Engine and Google Cloud services. You can sign-up via this formEarth Engine’s data catalog2) We are announcing expanded partnerships with these five geo-data focused independent software vendors (ISVs) to access sustainability datasets with low latency on Google Cloud: CARTO, Climate Engine, Geotab, NGIS, and Planet.Carto is a location Intelligence platform that enables organizations to use spatial data and analysis for more efficient delivery routes, better behavioral marketing, strategic store placements, and more.Climate Engine is an Enterprise-level deployment of Google Earth Engine. It provides organizations with a centralized system to ingest, process, and deliver Earth data into decision-making contexts. Geotab connects commercial vehicles to the internet and provides web-based analytics to help customers better manage their fleets. NGIS uses software and data to tackle issues such as sustainable development, biodiversity and conservation, preservation of Indigenous rights and interests, climate change and disaster risk reduction.Planet has a fleet of approximately 200 earth imaging satellites (the largest in history) to image the whole Earth land mass daily to deliver insights in agriculture, forestry, mapping, and government.Related ArticlePeople and planet AI: How to build a Time Series Model to classify fishing activities in the seaIn this episode of People & Planet AI, we share how to build a time series classification app that includes latitude and longitudinal fis…Read Article
Quelle: Google Cloud Platform

15. Oktober 2021

da Agency

Next Reaction: Making multicloud easier for all

If you’re like me, tracking all the news coming out of Google Cloud Next can be a bit overwhelming at times in a good way. There is just so much exciting stuff happening. So, in order to help us both out a bit I sat down to capture some of the key announcements that were made on the second day of Next, as well as provide some context around why I’m so excited about them. One of the overarching themes was how Google Cloud is making it easier for our customers to build and manage hybrid and multi-cloud environments. Coming from years of managing large enterprise environments this is all music to my ears. Developers and practitioners live in a world where they need to run code in a multitude of different environments. And, each of these environments usually comes with its own set of management tools. For years we’ve longed for the mythical “single pane of glass” that would provide us with a centralized place to manage and observe the disparate platforms where our apps were running. After hearing yesterday’s announcements I couldn’t be more excited about the direction Google Cloud is headed with respect to managing workloads everywhere that matters to the enterprise, from other public clouds to bare metal and VMs in their own data centers. Let’s jump in and look a bit deeper at what was announced. Anthos for VMsToday, a lot of organizations are looking to standardize on Kubernetes as their target platform for new applications. However, these same companies have hundreds of applications running in virtual machines, usually on VMware vSphere. This means that IT staff have to use one set of tools and processes for containerized workloads and another for VM-based workloads. That’s another pane of glass, if you’re counting.Anthos for VMs aims to reduce this complexity by allowing operators to centralize the management of VM-based applications with Anthos. You can use Anthos for VMs in a couple of different ways. First, if you have a large investment in VMware vSphere, and you’re not quite ready for a large-scale migration from VMs to containers, you can connect your vSphere instances to the Anthos control plane. This mode of operation doesn’t force you to move your workloads, but you still get a ton of benefits around centralized operational and security policies while also gaining insight into operational health via the Anthos dashboard. These VMs stay in place but are “attached” to your unified Anthos-based control plane.If your organization wants to migrate VM workloads off an existing virtualization platform to save on licensing costs or reduce complexity, Anthos for VMs can help there as well. Anthos for VMs uses Kubevirt, an open source solution for running VMs on Kubernetes, to allow you to “shift” your workloads from a traditional VM management platform to Kubernetes. Not only do you get the benefits I just mentioned around security policies and unified observability, but now you have a single set of tools for running and managing both your containerized and virtualized applications. If you’re like me, you’re probably wondering “What types of workloads should I be focused on migrating?” Anthos for VMs is a great choice for Virtual Network Functions (think virtualized firewalls, routers etc.) as well as monolithic applications. Even with that guidance there might still be a large pool of applications you could consider migrating. To help narrow down which applications are the best fit, you can use our updated fit assessment tool. This tool will examine your workloads and tell you how much effort might be involved in moving them. There are a couple things I really love about this announcement. First, this isn’t an all or nothing proposition. You can attach some of your vSphere instances to Anthos, shift another chunk of VMs to directly running on Anthos, and maybe leave some alone. Those decisions will be driven by what makes the most sense for your organization from both an IT as well as a business perspective. Making Multi-Cloud Easier With A Unified APIAnother big announcement from Next was around multi-cloud. Specifically the new Anthos Multi-cloud API. Anthos is gaining traction today because customers want to have a unified mechanism for deploying and managing workloads across different environments – including different cloud providers. Previously you could run Anthos clusters on AWS, and recently we introduced via preview support for Anthos clusters on Azure. With the release of Anthos Multi-cloud API, which is coming in Q4 2021, we’re making that even easier. This new API allows you to easily deploy and manage Anthos clusters across cloud providers with a unified set of tools: whether you use the command line, the API, or Google Cloud Console – you get a unified experience. Making Anthos Features Easier To Consume From GKE To Your Data CenterWhen I talk to customers about Anthos I often hear “I really like a lot of Anthos’s functionality, but I don’t really need everything it has to offer today. It’d be great if we could run just [feature or component X] on my existing GKE clusters.” Over the past year or so we’ve worked hard to address those types of requests. For instance, you can run both Anthos Service Mesh (ASM) and Anthos Config Management (ACM) on both Anthos and GKE clusters, with standalone pricing for ACM and ASM on GKE. And, yesterday we announced that ASM now supports hybrid deployment models – meaning you can have a single mesh spanning your cloud and on-prem resources. Again, another example of simplifying your tooling and processes by allowing you to leverage the same technology across multiple environments and deployment patterns. ConclusionToday’s announcements bring us one step closer to realizing the utopian vision of a single pane of glass. Now with Anthos you can consistently manage containerized workloads running across cloud providers as well as on-prem running on VMware or bare metal. Add into that the ability to manage VMs running on vSphere or on an Anthos cluster and your tool sets and processes have become vastly simplified.If you’ve not had a chance, be sure to watch yesterday’s announcements or read the blog post to get more details on this week’s launches. After that, head over to the Anthos page to learn how you can start reducing complexity and increasing flexibility with Anthos today. Related ArticleIntroducing Anthos for VMs and tools to simplify the developer experienceAt Google Cloud Next ‘21, we opened up Anthos to virtual machines, and revealed enhancements to our developer and operator tools.Read Article
Quelle: Google Cloud Platform

15. Oktober 2021

da Agency

Next Reaction: Security and zero-trust announcements

Trillions in Cybercrime?Phishing, spam, malware and devious websites? Oh my!I’ve talked a lot about Zero Trust security in the past, and the meme means many things to many people. For Google we want to make sure that security-across your cloud workloads, on-premises systems, collaboration tools and devices-is reliable and invisible. And today’s announcements help with that as Google builds security into more of the systems people use every day. That means better protection AND less work for us all as we protect our hapless, err… ‘focused’ employees from attack.One pretty cool example? Automatic Data Loss Prevention in BigQuery. So your sensitive data, such as phone numbers, credit card info, names, addresses, can be identified and protected from leaks, across your entire company. I see this as an extension of our Zero Trust philosophy: every network, device, person, service is untrusted until it proves itself. And that means data moving around, or access to internal systems, needs to be validated before being allowed.This year has also brought its share of high-profile cyberattack headlines, including ransomware at numerous big names and all manner of cryptomining or DDoS malware. I don’t see any sign of it decreasing either, as more and more companies make themselves appealing targets as they gather data and shift services to the internet.To help you protect yourself we’re extending the Zero Trust philosophy to your software supply chain, so that you can know exactly what software you’re building, deploying and shipping, with protection against unwanted changes that could compromise your data. I’m excited to see new helpful tools for creating and enforcing supply chain policy, as well as open source frameworks that can help you understand your software, like SLSA.Obviously it’s great to protect your key systems (and I hope you’re with me on that) but what about the employees and their devices? Attackers can usually get some malware onto an endpoint more easily than they can onto your infrastructure, and from there they just travel across and up to ‘explore’ for anything juicy to steal. So we need to protect people: enter Chrome threat protection!I love seeing innovation in this space: machine learning based URL checking to detect phishing sites in real-time, plus document detection, so we can help you do an in-depth scan of sketchy docs that might have malware but let benign attachments through quickly. On top of that new customized messages for malware and data loss prevention in the browser, so you can tune your communications to your employees or direct them to a good place to learn more about how you’re protecting them.As before: we want to make you more secure, but also make it easier at the same time. And these upgrades to WebProtect and BeyondCorp Enterprise help you do just that.I really enjoyed the Next ’21 announcements, and look forward to helping you all take advantage of the newest features in our security suite. Stay safe out there, and keep your data yours!Related ArticleBuild a more secure future with Google CloudHow Google Cloud secures the world with our people, platforms and products, announcements for Next 21Read Article
Quelle: Google Cloud Platform