Forrester Research has named Google Cloud a Leader in The Forrester Wave™: AI Infrastructure, Q4 2021 report authored by Mike Gualtieri and Tracy Woo. In the report, Forrester evaluated dimensions of AI architecture, training, inference and management against a set of pre-defined criteria. Forrester’s analysis and recognition gives customers the confidence they need to make important platform choices that will have lasting business impact. 

Google received the highest possible score in 16 Forrester Wave evaluation criteria: architecture design, architecture components, training software, training data, training throughput, training latency, inferencing throughput, inferencing latency, management operations, management external, deployment efficiency, execution roadmap, innovation roadmap, partner ecosystem, commercial model, and number of customers.

We believe that Google’s vision to be a unified data and AI solution provider for the end-to-end data science experience is recognized by Forrester, through high scores in the areas of architecture and innovation. We are focused on building the most robust yet cohesive experience to enable our customers to leverage the best of Google every step of the way. Here are four key areas where Google excels, among the many highlighted in this report. 

AI Infrastructure: Leverage the building blocks of innovation

When an organization chooses to run its business on Google Cloud, it benefits from innovative infrastructure available globally. Google offers users a rich set of building blocks such as Deep Learning VMs and containers, the latest GPUs/TPUs and a marketplace of curated ISV offerings to help architect your own custom software stack on VMs and/or Google Kubernetes Engine (GKE). 

Google provides a range of GPU & TPU accelerators for various use cases, including high performance training, low cost inferencing and large-scale accelerated data processing. Google is the only public cloud provider to offer up to 16 NVIDIA A100 GPUs in a single VM, making it possible to train very large AI models on a single node. Users can start with one NVIDIA A100 GPU and scale to 16 GPUs without configuring multiple VMs for single-node ML training.  Google also provides TPU pods for large-scale AI research with PyTorch, TensorFlow, and JAX.  The new fourth generation TPU pods deliver exaflop-scale peak performance with leading results in recent MLPerf benchmarks which included a 480 billion parameter language model.   

Google Kubernetes Engine provides the most advanced Kubernetes services with unique capabilities like Autopilot, highly automated cluster version upgrades, and cluster backup/restore. GKE is a good choice for a scalable multi-node bespoke platform for training, inference and Kubeflow pipelines, given its support for 15,000 nodes per cluster, auto-provisioning,  auto-scaling and various machine types (e.g. CPU, GPU, TPU and on-demand, spot). ML workloads also benefit from GKE’s support for dynamic scheduling, orchestrated maintenance, high availability, job API, customizability, fault tolerance and ML frameworks.  When a company’s footprint grows to a fleet of GKE clusters, its data teams can leverage Anthos Config Management to enforce consistent configurations and security policy compliance. 

Comprehensive MLOps: Build models faster and more easily without skimping on governance 

Google’s fully managed Vertex AI platform provides services for ML lifecycle management, from data ingestion and preparation all the way up to model deployment, monitoring, and management. Vertex AI requires nearly 80% fewer lines of code to train a model versus competitive platforms1, enabling data scientists and ML engineers across all levels of expertise to implement Machine Learning Operations (MLOps) so they can efficiently build and manage ML projects throughout the entire development lifecycle. 

Vertex AI Workbench provides data scientists with a single environment for the entire data-to-ML workflow, enabling data scientists to build and train models 5x faster than traditional notebooks. This is enabled by integrations across data services (like Dataproc, BigQuery, Dataplex, and Looker), which significantly reduce context switching.  Users are also able to access NVIDIA GPUs, modify hardware on the fly, and set up idle shutdown to optimize infrastructure costs. 

Organizations can then build and deploy models built on any framework (including TensorFlow, PyTorch, Scikit learn or XGBoost) with Vertex AI, with built-in tooling to track a model’s performance.  Vertex Training also provides various approaches for developing large models including Reduction Server to optimize bandwidth and latency of multi-node distributed training on NVIDIA GPUs for synchronous data parallel algorithms.  Vertex AI Prediction is serverless, and performs automatic provisioning and deprovisioning of nodes behind the scenes to provide low latency online predictions. It also provides the capability to split traffic between multiple models behind an endpoint. Models trained in Vertex AI can also be exported to be deployed in private or other public clouds.Google’s strengths in its current offering are in architecture, training, data throughput, and latency. Its sweet spot is in its product offering, Vertex AI, which has core AI compute capabilities and MLOps services for end-to-end AI lifecycle management.The Forrester Wave:™ AI Infrastructure, Q4 2021

In addition to building models, it is important to deploy tools for governance, security, and auditability. These tools are crucial for compliance in regulated industries, and they help teams to protect data, understand why given models fail, and determine how models can be improved. 

For orchestration and auditability, Vertex Pipelines and  Vertex ML Metadata tracks the inputs and outputs of an ML pipeline and the lineage of artifacts. Once models are in production, Vertex AI Model Monitoring supports feature skew and drift detection, alerting data scientists. These capabilities speed up debugging and create the visibility required for regulatory compliance and good data hygiene in general.For explainability, Vertex Explainable AI helps teams understand their model’s outputs for classification and regression tasks. Vertex AI tells how much each feature in the data contributed to the predicted result. Data teams can then use this information to verify that the model is behaving as expected, recognize bias in the model, and get ideas for ways to improve the model and training data.

These services together aim to simplify MLOps for data scientists and ML engineers, so that businesses can accelerate time to value for ML initiatives.

Security: Protect data while keeping ML pipelines flowing  

The Google stack builds security through progressive layers that deliver defense in depth. To accomplish data protection, authentication, authorization and non-repudiation, we have measures such as boot-level signature and chain-of-trust validation. 

Ubiquitous data encryption delivers unified control over data at-rest, in-use, and in-transit, with keys that are held by customers themselves. 

We offer options to run in fully encrypted confidential environments utilizing managed Hadoop or Spark with Confidential Dataproc or Confidential VMs.  

Partner Ecosystem: Work with world-class AI specialists 

Google works with certified partners globally to help our customers design, implement and manage complex AI systems. We have a growing list of partners with Machine Learning specializations on Google who have demonstrated customer success across industries, including deep partnerships with the largest Global System Integrators. The Google Cloud  Marketplace also provides a list of technology partners who allow enterprises to deploy machine learning applications on Google’s AI infrastructure.

Our dedication to being your partner of choice for ML Needs  

Leading organizations like OTOY, Allen Institute for AI and DeepMind (an Alphabet subsidiary) choose Google for ML, and enterprises like Twitter, Wayfair and The Home Depot shared more about their partnership with Google in their recent sessions at Google Next 2021.

Establishing well-tuned and appropriately managed ML systems has historically been challenging, even for highly skilled data scientists with sophisticated systems. With the key pillars of Google’s investments above, organizations can build, deploy, and scale ML models faster, with pre-trained and custom tooling, within a unified AI platform. 

We look forward to continuing to innovate and to helping customers on their digital transformation journey. To download the full report, click here. Get started on Vertex AI, learn what’s upcoming with infrastructure for AI and ML at Google here, and talk with our sales team.

From the historic Gold Country to the rugged heights of the Sierra Nevada, Placer County encompasses more than 1,500 square miles—and provides services to nearly 400,000 residents. Across the county, residents access resources in person, over the phone, and through the county website. In 2018, the county piloted a suite of eServices through its Community Development Resource Agency (CDRA) with the goal of helping its geographically dispersed population more easily apply for permits, make appointments, and get immediate answers to specific questions.

Now, with the help of Google Cloud Dialogflow and Speech-to-Text API, the county has created a virtual agent that enables anyone, anywhere, to simply start a conversation by saying “Ask Placer County” into a variety of voice assistant hardware. This virtual agent builds on the success of eServices and helps the public access information through their smartphones or smart home devices, which is especially helpful for individuals without immediate access to a computer—such as the traveling public or a contractor working on-site, for example.

The pilot program has been an opportunity to explore new technology and create another communication channel to our citizens. “Our ambitious and long-term goal is that ‘Ask Placer County’ will be like having a personal assistant to everything related to Placer County. While the virtual agent is currently limited to a few departments, we plan to expand it countywide,” says Ben Palacio, senior IT analyst for Placer County.

“Our ambitious and long-term goal is that ‘Ask Placer County’ will be like having a personal assistant to everything related to Placer County. While the virtual agent is currently limited to a few departments, we plan to expand it countywide.”

—Ben Palacio, Senior IT Analyst, Placer County

New eServices get a boost from AI virtual agent

The CDRA, one of many departments and agencies within Placer County, deployed interactive eServices for residents. As part of this initiative, the CDRA made a special request to the information technology (IT) department: Build an AI-based virtual agent that would spotlight the eServices on the website and make them easily available through a conversational interface.

“Having a specific technology requirement voiced by an agency was visionary and very welcome. It meant they were engaged and excited about the possibilities that these new technologies had to offer,” says Mike Spak, IT Manager for Placer County.

Today, the CDRA’s eServices help residents answer important questions, such as: “Is my land zoned for adding a garage?” “What historical engineering work has been done on my property?” And, “How can I make an appointment at a permitting office?”

“Having a specific technology requirement voiced by an agency was visionary and very welcome. It meant they were engaged and excited about the possibilities that these new technologies had to offer.”

—Mike Spak, IT Manager, Placer County

Google Cloud fit easily into Placer County’s multivendor environment

With the help of consultants from Dito, a Google Cloud Premier Partner, Placer County chose Google Cloud to help with “Ask Placer County.”

Placer County runs a multivendor IT environment. “Especially with the cloud, the easier it is to integrate a vendor’s capabilities with others, the better for everybody,” says Palacio. “Google Cloud was able to integrate with other vendors’ capabilities for this project.”

For example, Placer County uses the Google Cloud operations suite (formerly Stackdriver) to monitor, troubleshoot, and improve its cloud infrastructure and application performance. And it uses Dialogflow, which powers the natural language processing (NLP) interface of the “Ask Placer County” virtual agent. Both integrate easily with tools from other vendors.

“Especially with the cloud, the easier it is to integrate a vendor’s capabilities with others, the better for everybody. Google Cloud was able to integrate with other vendors’ capabilities for this project.”

—Ben Palacio, Senior IT Analyst, Placer County

Real-time conversations powered by “Ask Placer County”

Dito got “Ask Placer County” up and running using App Engine and Datastore. Today, the “Ask Placer County” virtual agent provides information ranging from how to adopt a pet to short-term-rental compliance.

Users can say “Ask Placer County” into their smartphones or their computer microphones and access targeted information from the pilot departments. IT staff reviews the asked questions and are constantly updating the virtual agent to provide more accurate and responsive information.

“You could be in your backyard with a contractor trying to get a project started, and if the contractor has a question about permitting or zoning, they can use their smartphone to get an answer right then and there, rather than having to interrupt the meeting, get back in their car, and drive to a county office,” says Palacio.

The county hopes “Ask Placer County” will improve the efficiency of in-person interactions between the public and county employees as well. Because the public can access common questions and information online or through the virtual agent, employees’ interactions with the public can be more focused and productive.

“We are providing more effective and efficient service to our customers through 24/7 access to information and by reducing the proportion of staff’s time in responding to emails and voicemails to answer our customer questions,” says Shawna Purvines, Principal Planner for Placer County.

According to Placer County, the virtual agent currently answers a monthly average of around 200 questions for the CDRA, and the county continues to develop more and more complete answers across participating departments.

“We are providing more effective and efficient service to our customers through 24/7 access to information and by reducing the proportion of staff’s time in responding to emails and voicemails to answer our customer questions.”

—Shawna Purvines, Principal Planner, Placer County

Constantly evolving based on users’ needs

To best serve the needs of the CDRA, and potentially other departments over time, “Ask Placer County” continues to evolve. The county can add and retire questions based on the needs of constituents. This technology can be tailored to address current needs, such as responding to COVID-19. “Our hope is that, in quickly evolving situations, the virtual agent can be a resource for the public to access real-time information about public services,” says Palacio.

As a component of the eServices group, “Ask Placer County” has kept construction and development in Placer County operational during the COVID-19 pandemic, and it continues to provide opportunities for the public to access county resources without coming in to the counters, reducing the need for in-person visits. In fact, according to Placer County data, permit applications have increased by 17%.

Placer County also performs analytics using the Google Cloud operations suite to gain insights into the most popular questions and, just as importantly, the questions that are not being answered. “We get insight into what we’re missing—for those times when the virtual agent can’t find a response in our database,” says Palacio. Placer County uses these insights to improve answers and ultimately improve the efficacy of the virtual agent over time.

“This continual, real-time learning is the exciting part of the application that lets us help constituents in entirely new ways. It’s really cool,” says Palacio.

County IT workers actively analyze CDRA webpages to determine which are the most visited and to craft questions and answers to add to “Ask Placer County.” According to Placer County, the number of questions the virtual agent can answer is now more than 600. But the CDRA pages are just a small subset of the 5,000 pages that make up the Placer County website. A future goal is to provide a backstop for customer questions that can’t be answered, transferring those customers to county staff for resolution.

“Looking ahead, this technology has the ability to provide answers to thousands and thousands of questions,” says Palacio.

“This continual, real-time learning is the exciting part of the application that lets us help constituents in entirely new ways. It’s really cool.”

—Ben Palacio, Senior IT Analyst, Placer County

Scaling “Ask Placer County” countywide

The limited rollout of the virtual agent technology allowed IT to monitor its effectiveness and make initial adjustments. As the virtual agent evolves, the Placer County chief information officer sees opportunities for it to engage the public countywide.

The “Ask Placer County” virtual agent can truly be a concierge, helping the public navigate county resources. It can help citizens become more engaged with their elected officials, it can provide information, and it can more efficiently connect the public with the services they seek.

Some of the future possibilities include providing current information on board meetings and individual elected officials, checking on permit status and burn days, and getting the latest county news. “There are so many ways we can use this solution to tackle issues within the county that we’re going to have to somehow prioritize them,” says Palacio.

The increasing adoption of technologies like connected devices, augmented reality, and machine learning has changed the way we shop, and retailers are evolving how they do business to meet the needs of their customers.

Retailers say it’s no longer enough to keep pace with shoppers’ growing expectations—they must get ahead of them. That’s why more and more are turning to the cloud. They’re using it to eliminate data silos and take advantage of cloud-based analytics. They’re tapping into machine learning to improve all aspects of the value chain. And they’re making use of reliable and secure cloud infrastructure to scale their businesses.

Although every retail customer is different, many of them share similar objectives. Here are three major ways retailers take advantage of the cloud.

Storing and Analyzing Data in the Cloud

Data presents both a challenge and an opportunity for retailers. Which is why Ulta Beauty, the largest beauty retailer in the US, is moving to Google Cloud Platform (GCP). Now, with the help of BigQuery, Ulta Beauty will be able to more efficiently predict and analyze outcomes and develop more meaningful data insights that can be leveraged to deliver a more personalized, relevant guest journey.

They are not alone. DSW has also chosen to use GCP to help relaunch their DSW VIP loyalty program for the first time in over 10 years. With more than 90% of transactions running through their loyalty program, DSW needed a flexible and scalable solution to deliver a real-time loyalty program for 26 million active members. They’ve already seen a 9% uptick in new customers and have improved their already strong retention rate.

Improving Customer Experiences with AI and Machine Learning

Once retailers are able to access these insights, they are turning to AI to help personalize the overall shopping experience. At first, retail companies leveraged AI tools such as machine learning for product recommendations.

Now, more retailers use AI to forecast trends, predict inventory needs and prevent stock outs, and provide personalized recommendations to customers to intelligently and efficiently serve them.

Just look at METRO AG, one of the largest B2B wholesalers globally. They’re using AI and machine learning to better serve their customers. For example, many of their customers are restaurant owners. With Google Cloud AI capabilities, they can create tools that identify when a restaurant is out of a particular ingredient and automatically order more.

Ocado is another great example. The world’s largest online-only grocery retailer drove a 3.5% increase in contact center efficiency by using Google Cloud machine learning technology to respond to customer emails four times faster.

To help businesses further accelerate their AI solutions, Google has developed the Advanced Solutions Lab (ASL), which gives businesses the opportunity to work side-by-side with Google’s AI and ML experts to solve high impact challenges.

Fast Retailing, the Japanese retailer behind Uniqlo, is working with Google Cloud and ASL to help them better analyze customer data to forecast demand and deeply understand what their customers want.

Carrefour, one of the world’s leading retailers, also announced last year that its engineers will be working side-by-side with our AI experts to co-create new consumer experiences. This is in addition to deploying G Suite to their employees to support the company’s digital transformation.

Scaling Infrastructure to Meet Demand

Of course, none of this innovation is possible without a reliable infrastructure that can scale instantly to meet surges in traffic.

And many have found the reliability and security they need with the cloud. That’s why global cosmetics brand Lush chose Google Cloud. They migrated their e-commerce platform to GCP to handle increased traffic without compromising stability.

This move that ultimately reduced infrastructure hosting costs by 40 percent.

L.L.Bean also modernized its IT infrastructure by moving capabilities from its on-premises systems to GCP, improving customer satisfaction and IT efficiency across multiple sales channels.