Providing best-in-class customer service is crucial for the success of your business. Contact centers are a critical touch point, as they have to balance between representing your brand and prioritizing customer care. When your customers seek help and support, they expect efficient service that is accessible through modern voice and digital channels. In short, customer expectations are increasing—and that’s a problem if your contact center infrastructure and solutions are becoming outdated.

All of these factors are why today, we’re announcing Google Cloud Contact Center AI Platform, an expansion to Contact Center AI that offers an out-of-box, end-to-end solution for the contact center. It brings together the advantages of AI, cloud scalability, multi-experience capabilities, and tight integration with customer relationship management (CRM) platforms to unify sales, marketing, and support teams around data across the customer journey.

Improving customer experiences from all angles
Google Cloud’s Contact Center AI helps you leverage AI to scale your contact center interactions while maintaining a high level of customer satisfaction. Over the last two years, we have built a large group of partners, including the largest contact center and customer experience ISVs and our system integrator ecosystem, to bring Contact Center AI to customers. Today, we are helping enterprises across industries and geographies to cost-effectively reimagine contact center experiences. For example, Marks & Spencer reduced in-store call volume by 50%, and similarly, The Home Depot improved call containment by 185%, all while significantly increasing customer self-service engagement.

Adding to our Contact Center AI capabilities, Contact Center AI Platform is purpose-built for customer relationship management, extending your ability to offer personalized customer experiences that are consistent across your brand, whether delivered through a virtual agent, a human agent, or a combination of both. It eliminates many long-running pain points, from managing data fragmentation to replacing rigid customer experience flows with more engaging, personalized, and flexible support. With this addition, Contact Center AI now lets you:

Orchestrate the customer journey by creating modern experiences that can be embedded in their chosen channels with mobile/web software developer kits (SDKs), compatible with iOS and Android;

Leverage CRM as a single source of insight into the customer experience, to unify content, increase personalization, and automate processing with CRM data unification;

Manage multiple channels without pivoting across voice, SMS, and chat support;

Predict customer needs and route calls appropriately with AI-driven routing, based on both historical CRM data and real-time interactions;

Automate scheduling, schedule adherence monitoring, and manage employee scheduling preferences with Workforce Optimization (WFO) integration;

Provide customers with self-service via web or mobile interfaces using Visual Interactive Voice Response (IVR).

Helping you do more with contact centers
The addition of Contact Center AI Platform provides your partners the ability to integrate with Contact Center AI, so you can enjoy a more seamless experience operating your customer service center, with a complete view of the customer in a single workspace that includes real-time AI intelligence, native agent call controls, and real-time call transcription. For example, we are expanding our partnership with Salesforce to integrate Contact Center AI with Service Cloud Voice to deliver a unified Service Cloud agent console and Customer 360.

“Customers are continually raising their service expectations, and our research tells us 79% of consumers believe the experience a company provides is as important as its products and services,” said Ryan Nichols, SVP & GM, Contact Center, for Salesforce Service Cloud. “Through intelligence, workflows, and a deeper understanding of the customer, Salesforce’s Service Cloud Voice paired with Google’s Contact Center AI will empower agents with a seamless experience to help them wow customers.”

We are also excited to partner with UJET, an innovative and experienced Contact Center as a Service (CCaaS) provider. UJET offers secure user-centric design, scalability, and mobile-focused solution, with turnkey implementation, strong omnichannel capabilities, and best-in-class user experience, making their product a natural fit into Google’s contact center vision. To learn more about the partnership, see here.

Delivering impact for customers
Contact Center AI is already making a difference for our customers such as OneUnited Bank, the largest Black-owned bank in the U.S. “OneUnited Bank has been in partnership with Google Cloud and UJET, as well as a long-standing customer of Salesforce. The expansion and enhancements of Google Cloud’s Contact Center AI, along with its deeper integration with Salesforce, means better return on investment as we drive towards evolving our contact center to deliver exceptional client experiences,” said Teri Williams, President and Chief Operating Officer at OneUnited Bank.

Fitbit, which boasts more than 29 million active users, is also reaping the benefits. “Fitbit relies on Google Cloud and UJET to provide support to our customers with a mobile-first approach. This collaboration, in combination with a strong Salesforce integration, has helped us modernize our entire customer support experience,” stated Cassandra Johnson, VP, Devices & Services Customer Care & Vendor Management Office, at Google.

According to industry analyst Sheila McGee-Smith of McGee-Smith Analytics, “Google Cloud’s Contact Center AI is already a force in the contact center industry thanks to its early focus on AI for customer experience.” She continued, “Through their partnerships with UJET and Salesforce, as well as these expanded capabilities, Google Cloud’s Contact Center AI Platform will help define the future of customer service by powering more secure, engaging, and personalized customer experiences.”

Contact Center AI Platform is supported by a host of integration partners, including Accenture, CDW, Cognizant, Deloitte, HCL, IBM, Infosys, Quantiphi, Tata Consultancy Services, and Wipro. We will also continue to partner closely with the contact center and customer experience (CX) ISVs that our customers already rely on. If you already have a contact center solution provider, you can still integrate Google Cloud’s Contact Center AI into your existing environment.

To learn more about how you can leverage the power of AI to reimagine your contact center experience, visit our Contact Center AI page.

Today, ML-driven innovation is fundamentally transforming computing, enabling entirely new classes of internet services. For example, recent state-of-the-art lage models such as PaLM and Chinchilla herald a coming paradigm shift where ML services will augment human creativity. All indications are that we are still in the early stages of what will be the next qualitative step function in computing. Realizing this transformation will require democratized and affordable access through cloud computing where the best of compute, networking, storage, and ML can be brought to bear seamlessly on ever larger-scale problem domains.

Today’s release of MLPerf™ 2.0 results from the MLCommons® Association highlights the public availability of the most powerful and efficient ML infrastructure anywhere. Google’s TPU v4 ML supercomputers set performance records on five benchmarks, with an average speedup of 1.42x over the next fastest non-Google submission, and 1.5x vs our MLPerf 1.0 submission. Even more compelling — four of these record runs were conducted on the publicly available Google Cloud ML hub that we announced at Google I/O. ML Hub runs out of our Oklahoma data center, which uses over 90% carbon-free energy.

Let’s take a closer look at the results.

Performance at scale…and in the public cloud

Our 2.0 submissions1, all running on TensorFlow, demonstrated leading performance across all five benchmarks. We scaled two of our submissions to run on full TPU v4 Pods. Each Cloud TPU v4 Pod consists of 4096 chips connected together via an ultra-fast interconnect network with an industry-leading 6 terabits per second (Tbps) of bandwidth per host, enabling rapid training for the largest models.

Hardware aside, these benchmark results were made possible in no small part by our work to improve the TPU software stack. Scalability and performance optimizations in the TPU compiler and runtime, including faster embedding lookups and improved model weight distribution across the TPU pod, enabled much of these improvements, and are now widely available to TPU users. For example, we made a number of performance improvements to the virtualization stack to fully utilize the compute power of both CPU hosts and TPU chips to achieve peak performance on image and recommendation models. These optimizations reflect lessons from Google’s cutting-edge internal ML use cases across Search, YouTube, and more. We are excited to bring the benefits of this work to all Google Cloud users as well.

Translating MLPerf wins to customer wins

Cloud TPU’s industry-leading performance at scale also translates to cost savings for customers. Based on our analysis summarized in Figure 3, Cloud TPUs on Google Cloud provide ~35-50% savings vs A100 on Microsoft Azure (see Figure 3). We employed the following methodology to calculate this result:2

We compared the end-to-end times of the largest-scale MLPerf submissions, namely ResNet and BERT, from Google and NVIDIA. These submissions make use of a similar number of chips — upwards of 4000 TPU and GPU chips. Since performance does not scale linearly with chip count, we compared two submissions with roughly the same number of chips.

To simplify the 4216-chip A100 comparison for ResNet vs our 4096-chip TPU submission, we made an assumption in favor of GPUs that 4096 A100 chips would deliver the same performance as 4216 chips.

For pricing, we compared our publicly available Cloud TPU v4 on-demand prices ($3.22 per chip-hour) to Azure’s on-demand prices for A1003 ($4.1 per chip-hour). This once again favors the A100s since we assume zero virtualization overhead in moving from on-prem (NVIDIA’s results) to Azure Cloud.

The savings are especially meaningful given that real-world models such as GPT-3 and PaLM are much larger than the BERT and ResNet models used in the MLPerf benchmark: PaLM is a 540 billion parameter model, while the BERT model used in the MLPerf benchmark has only 340 million parameters — a 1000x difference in scale. Based on our experience, the benefits of TPUs will grow significantly with scale and make the case all the more compelling for training on Cloud TPU v4.

Have your cake and eat it too — a continued focus on sustainability

Performance at scale must take environmental concerns as a primary constraint and optimization target. The Cloud TPU v4 pods powering our MLPerf results run with 90% carbon-free energy and a Power Usage Efficiency of 1.10, meaning that less than 10% of the power delivered to the data center is lost through conversion, heat, or other sources of inefficiency. The TPU v4 chip delivers 3x the peak FLOPs per watt relative to the v3 generation. This combination of carbon-free energy and extraordinary power delivery and computation efficiency makes Cloud TPUs among the most efficient in the world.4

Making the switch to Cloud TPUs

There has never been a better time for customers to adopt Cloud TPUs. Significant performance and cost savings at scale as well as a deep-rooted focus on sustainability are why customers such as Cohere, LG AI Research, Innersight Labs, and Allen Institute have made the switch. If you are ready to begin using Cloud TPUs for your workloads, please fill out this form. We are excited to partner with ML practitioners around the world to further accelerate the incredible rate of ML breakthroughs and innovation with Google Cloud’s TPU offerings.

1. MLPerf™ v2.0 Training Closed. Retrieved from https://mlcommons.org/en/training-normal-20/ 29 June 2022, results 2.0-2010, 2.0-2012, 2.0-2098, 2.0-2099, 2.0-2103, 2.0-2106, 2.0-2107, 2.0-2120. 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.
2. MLPerf v1.0 and v2.0 Training Closed. Retrieved from https://mlcommons.org/en/training-normal-20/ 29 June 2022, results 1.0-1088, 1.0-1090, 1.0-1092, 2.0-2010, 2.0-2012, 2.0-2120.
3. ND96amsr A100 v4 Azure VMs, powered by eight 80 GB NVIDIA Ampere A100 GPUs (Azure’s flagship Deep Learning and Tightly Coupled HPC GPU offering with CentOS or Ubuntu Linux) is used for this benchmarking
4. Cost to train is not an official MLPerf metric and is not verified by MLCommons Association. Azure performance is a favorable estimate as described in the text, not an MLPerf result. Computations are based on results from MLPerf v2.0 Training Closed. Retrieved from https://mlcommons.org/en/training-normal-20/ 29 June 2022, results 2.0-2012, 2.0-2106, 2.0-2107.

Local governments are embracing more modern and scalable ways to support their communities. In an effort to save both time and money, Costa Mesa Sanitary District  (“CMSD”) used machine learning to automate and streamline manhole maintenance. Manhole maintenance is an essential part of the upkeep of cities. Manholes provide critical access points for underground public utilities, allowing inspection, maintenance, and system upgrades. But failure to upkeep manholes can cause a multitude of problems, from road hazards to sewer blockages, and can make it difficult for workers to access underground public utilities, which can lead to other safety issues. Manhole maintenance is an essential part of Costa Mesa Sanitary District maintenance, but this process requires the work of an outside consultant and costs the CMSD over $100,000.

ML to the rescue 

CMSD, in collaboration with SpringML and Google Cloud, developed a project to streamline manhole maintenance by leveraging the power of machine learning (ML) to detect sewer manholes and rate their conditions. This solution saves CMSD $40,000 every year, freeing up funds for other public service projects.

Every quarter, one member of the CMSD drives a car outfitted with a GoPro camera. This car travels through the entire District area, which includes the city of Costa Mesa and small portions of Newport Beach, CA, which is about 218 street miles, and records the roads to detect approximately 5,000 manholes. At the end of the recording day, CMSD members transfer images and videos from the GoPro SD card into a local server. Then these files are automatically ingested into Google Cloud Storage for processing. From here, the machine learning algorithms detect which manholes need repair.

Google Cloud products are used throughout this project. Once the images and videos are in Google Cloud Storage, a workflow with Cloud Scheduler spins up the VM every night to detect if there are new videos on Cloud Storage. If there are, this triggers cloud machine learning, which reviews the numerous images and videos, rates each manhole, and determines if any require maintenance. 

Machine learning to detect and grade manholes

SpringML applied a very systematic approach to detecting and grading the manholes. First, image processing ensures that the region of interest is only the section of the road in front of the vehicle to avoid any privacy concerns. Then SpringML applied a 5-step process using machine learning to detect and grade the manholes.

1. SpringML developed two separate custom TensorFlow-based Mask R-CNN models. Mask-R-CNN is a deep neural network that is used for image segmentation tasks, which means it can separate different objects in an image or a video. 
   
   The first Mask-R-CNN model was created to accurately detect if an image showed a manhole cover. This was a critical step because sewer manhole covers look similar to water main covers. To make the model accurate, it was important that there be no false positives. SpringML used around 50 images of sewer manholes and other images to train and validate that the algorithm could successfully detect sewer manhole covers. 
2. Once a manhole is accurately detected, the surrounding area is masked using image processing to focus on the manhole and then the cropped images are sent to the second model which is used to detect damage in the area around the manhole, called the apron. To reduce the processing time, the second model only does the inference if a manhole cover is detected.
3. The second Mask-R-CNN model uses CMSD Guidelines to decide what types of damage contributed to the rating system. For training purposes, SpringML uses a TensorFlow-Keras inside a Virtual Machine on Google Cloud. The initial model was trained and the Keras weights were saved on Google Cloud Storage. This helped create a versioned system of the models as the model gets refined over time.
4. Then, duplicated detections are cleaned up to have a unique detection for each manhole cover.
5. Finally, Manholes are graded from a 1-5 rating, with 5 representing high damage and 1 representing low damage. 

Once cloud machine learning has analyzed the new videos and images, the final scores are stored in BigQuery. Results are then served to members of CMSD via a simple web application where they can see which manholes need to be maintained. Two staff members review the ML results and determine priorities for repairs. One of the most interesting features of this project is that the model gets continually retrained based on feedback submitted in the web application. For example, if the model inaccurately detects a manhole, a member of CMSD can mark that in the web application and their feedback is immediately used to refine the model.

This solution shows how leveraging machine learning can streamline a necessary local government project and save money and labor while being highly scalable! Not only does this system streamline the manhole maintenance process, but it also allows for more frequent review of manhole conditions and provides a historical view of how the District’s manholes change over time.

Want to learn more about Google Cloud machine learning? Check out this tutorial to  learn TensorFlow and Keras and check out more machine learning tools in our AI Platform.

Verve Group is an ecosystem of demand and supply technologies fusing data, media, and technology to deliver results and growth to both advertisers and publishers – no matter the screen or location, no matter who, what, or where a customer is. Classifying massive amounts of this unstructured data at scale is the first step in helping to surface relevant, high-quality content to users—and that’s where natural language processing (NLP) comes in.

Verve Group uses the NLP API from Google Cloud’s Vertex AI to fetch data for their internal content classification quality verification and as an additional source for building categorization models. By leveraging the NLP API’s Content Classification models, which are now generally available and offer Google’s latest large language model (LLM) technology, Verve Group powers classification through an updated and expanded training data set with over 1,000 labels and support for 11 languages (Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch join previously-available English). 

Verve Group has been using Google Cloud’s NLP API since day one, because of both the ease of implementation and the quality compared to competing NLP products. With documentation that is “comprehensive and self-explanatory,” the NLP API “allows for fast adoption and implementation, from test models all the way to production,” said Rami Alanko, GM of Verve Group.

Leveraging the NLP API has facilitated Verve Group’s fast go-to-market motions by enabling its customers to quickly discover and classify new content. “Operating on a global level with tens of different regions and languages, we have still been able to maintain high quality for our product and high retention rates with our clients,” Rami shared. “In a recent client case, we achieved 82% improvement in CTR when optimized with content quality measurements enabled by the API. In another client case, we drove brand safety risk down to 0.16% from 4% thanks to classification quality. Along with the new functionalities of the Google NLP, I can only see this trend continuing to strengthen.”

Verve Group is excited to further expand their NLP use cases by leveraging the new Content Classification models, which have already helped them expand their classification inventory, improve the quality and performance of their quality verification for customers, and unlock new use cases for NLP. “Our classification model accuracy improved 41% using Google NLP as a verification partner,” said Rami.  

Additionally, Verve Group is now using the API for metadata analysis on a  large image database. “We browse the database and run the image metadata via our classification. This flow enables us to classify images reliably aligned with our standard classification.  We pretty much use the same data flow for our runtime in-app textual content analysis, therefore allowing for close to real-time consumer engagement,” Rami added.

To learn more about how companies are leveraging NLP API from Google Cloud Vertex AI, click here, and to learn more about Google Cloud’s work with foundation models and generative AI, read The Prompt on Transform with Google Cloud.