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.

For more than 10 years, NMK Textile Mills has manufactured bed linens for major retailers in the United States and Canada. As e-commerce exploded, the company’s co-founder saw an opportunity to grow the business. So, in addition to manufacturing bed linens wholesale for retail customers, NMK Textile Mills reworked its complete supply chain to manufacture products for California Design Den, which became an e-commerce retailer selling its own fashion-forward products directly to consumers online.

With California Design Den’s push into e-commerce, it became apparent the SMB company (with about 250 global employees) faced the same tough supply chain questions as large retail customers, including maintaining enough inventory to meet customer demand in a timely, efficient way.

California Design Den depended upon a myriad of systems to track its complex forecasting and reordering processes. Team members typically planned inventory manually using desktop spreadsheet software, which could lead to excess inventory. Accurately forecasting demand and supply was essential to the company’s financial success—but it was also a challenge.

A couple years ago, California Design Den partnered with Pluto7, a technology solutions provider that offers a software as a service (SaaS) called Planning In A Box. Leveraging Google Cloud Platform machine learning and artificial intelligence, Planning In A Box intelligently helps predict demand and balances it with supply.

But that was just the beginning. “Along the way, we realized that to compete with larger retailers, make quicker decisions, and move faster, we needed to go further,” says Deepak Mehrotra, Co-founder and Chief Adventurer at California Design Den.

With guidance from Pluto7, California Design Den began migrating its database to Google Cloud Platform. Using Google BigQuery, Google Compute Engine, Google Cloud SQL, and Google Cloud Storage, and experimenting with Google Cloud Vision and Google Cloud AutoML, the company is reducing inventory carryovers by more than 50%, improving the accuracy of demand planning quarter over quarter, and gaining granular insights into how individual SKUs are performing.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels. With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

No need for army of data scientists

“Using Google Cloud Platform machine learning and AI was essential for California Design Den if it was to compete successfully with larger retailers,” Deepak says.

For example, tastes and fashions in bed linens can change quickly and consumer prices fluctuate. With more than 2,500 SKUs, it wasn’t possible for California Design Den’s team to continuously monitor product demand and experiment with competitive pricing in real time.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels,” says Deepak. “With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

By integrating all its data onto Google Cloud Platform, California Design Den’s team gains deeper insights into product sales over time, which in turn helps the company improve demand planning by better determining which styles to manufacture and sell in the future.

“When experienced employees leave, their knowledge leaves with them. By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Merging visuals with data

Before Google Cloud Platform, team members had to dig through spreadsheets and run scenarios to get a sense of how particular products had sold. The next step was to perform keyword searches across the company’s photo library in the cloud to find each product’s image. From there, a team member would insert the product images into a presentation, along with relevant data points, to provide a report for stakeholders on how particular styles performed.

Today, California Design Den, with the help of Pluto7, is integrating its entire product image library with its database on Google Cloud Platform. Experimenting with Google Cloud Vision and Google Cloud AutoML, California Design Den is moving towards a day when team members can run sales scenarios and get deep background data on individual product performance while viewing images of the relevant products.

Merging product visuals with data will help designers and team members better understand sales patterns over time and in context. In the past, making correlations between things like which sheet colors sold well in California, compared to how the same sheet colors performed on the East Coast, was something that California Design Den employees primarily did in their heads.

“When experienced employees leave, their knowledge goes with them,” says Manjunath Devadas, Founder and CEO at Pluto7. “By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Reimagining supply-demand balancing

Pluto7’s mission statement is to democratize supply demand balancing with machine learning and AI. California Design Den is a case in point, as the combination of Planning In A Box and Google Cloud Platform gives the company greater control over its destiny.

“Big retailers used to tell us what to manufacture and how much they would pay for it,” says Deepak. “That was our primary business, and if we didn’t accept the terms, a competitor would.” Today, in addition to continuing to make products for retailers, California Design Den can design, make, and sell a variety of designs for itself, including custom and limited-edition products, thanks to Google Cloud Platform and Pluto7 software offerings. The payoff is not only in having a more diversified business. California Design Den also receives more favorable profit margins by selling its own products.

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything,” Deepak says. “We can make smaller batches. We can connect directly with consumers. We can identify the missing pieces—what should we produce next, when, and how much? We otherwise couldn’t afford the level of talent it would take to do this.”

Cutting through the noise

Google and Pluto7 software helped California Design Den reduce inventory carryovers by more than 50%. Inventory tracking and distribution, along with insights and visibility into product sales, are faster, more efficient, and accurate. Google Cloud Platform flexible pricing, speed, reliability, security, and scalability enable California Design Den to stay relevant and be more competitive.

In addition to benefiting from Google Cloud Platform, California Design Den relies on G Suite—also part of Google Cloud—to enhance collaboration among its global teams. Previously, the company’s email server would sometimes crash, due to the heavy load of sharing product photos and other data. “Gmail and Google Drive handle the everyday demands on the business effortlessly and reliably,” Deepak says.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important. We can focus on analytics to guide us to success today and in the future.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

The company is exploring additional ways to leverage Google Cloud Platform in the near future. For example, one possibility is to import customer reviews from sites where products are sold into Google BigQuery, and to use that data to perform sentiment analysis via Google Cloud Natural Language. It could provide another valuable data source to help California Design Den’s team decide where to focus future designs.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important,” Deepak says. “We can focus on analytics to guide us to success today and in the future.”

Whether they meet customers online, offline, or in some combination, retailers share a big problem: How can they offer the right choices, when and how the customer wants, without overwhelming (and often losing) the buyer?

More than anything, this is an information problem. As such, it’s a good candidate for using artificial intelligence (AI) for greater success. Here’s how Richemont tackled the problem.

Richemont owns a portfolio of leading luxury goods brands, recognized for their distinctive heritage, craftsmanship and creativity. It has strengths and specialties in jewelry (Cartier, Van Cleef & Arpels), luxury watches (IWC, Jaeger-LeCoultre, Panerai, Vacheron Constantin), and fashion & accessories (Chloé, Montblanc, dunhill).

People shop for such goods in a number of ways, from online searching to individual meetings in boutiques, and Richemont must be prepared for every context. Understanding which shoppers are likely to buy or repurchase, when to engage directly, and what creation to suggest enables sales associates to spend quality time with clients, engaging at the right time with meaningful advice. Richemont solves these retail challenges with an integrated Client Platform leveraging Google Cloud and its AI/ML capabilities.

Enticing peoples’ desires with Machine Learning

Richemont began by posing two questions:

1. Which prospects or clients need extra attention? Specifically, who is likely to convert or to repurchase?
2. What would be meaningful items to suggest to each client and prospect?

Both questions were addressed with machine learning algorithms. Their challenges included deploying and monitoring algorithms at scale for several brands across the globe, while addressing the specific business needs for each brand. For instance, it may be more relevant to recommend in-season items for fashion brands, while for watchmakers it is more about cross-fertilization across each brand’s iconic creations.

This graph summarizes the prediction process implemented by Richemont:

Engagement data (email opened, clicked, SMS/MMS, website visits…) was found crucial to predict conversion of prospects for whom per definition no transaction history is available. For website interactions Richemont leverages the Google x Salesforce Connector.

To deploy the Machine Learning algorithms and to monitor them, Richemont leveraged Vertex AI, along with BigQuery, Cloud Functions and Google Storage, all orchestrated with Google Cloud Composer.

The role of product recommendation algorithms

Richemont used the deep learning library TensorFlow Recommenders to perform the product recommendation tasks. This library enables companies to build state of the art deep learning algorithms to achieve relevant and robust predictions.

Unlocking client value with integrated technology

Richemont’s innovations show how technology that considers many parts of the customer experience creates more value. In this case, the company used in store applications to invite people with a strong propensity to buy for boutique visits, while others at a different point in the purchasing journey were offered different options more suited to their tastes and inclinations.This solution, now deployed across 11 brands in over 25 countries, shows just one way that AI can improve customer experience, for better customer loyalty.

Key to the process, here and elsewhere, is the way a retailer and its partners put customer understanding at the center of the process. As AI becomes more important not only in retail, but in every industry, this human understanding will become even more important as a fundamental organizing principle. Much is changing, but once again, the winners will be the companies that focus best on their customers.