In recent years, the grocery industry has had to shift to facilitate a wider variety of checkout journeys for customers. This has meant ensuring a richer transaction mix, including mobile shopping, online shopping, in-store checkout, cashierless checkout or any combination thereof like buy online, pickup in store (BOPIS).  

What’s more, in the past year and a half alone grocers have had to enable consumers new ways to shop for essentials. This has included needing to rapidly integrate or build on-demand delivery apps, offer curbside pickup with near-instant fulfillment as well as support touchless and cashless checkout experiences. Searches on Google Maps for retailers in the US with curbside pickup options have increased by 9000% since March 2020, and we believe these trends from 2020 will continue to define the future of grocery shopping.

The future of grocery will require agility and openness

Firstly, the need to rapidly adapt to changing consumer habits will be the new normal. Grocers will increasingly look to digitally transform legacy retail systems and modernize point of sale (POS) platforms to deliver and scale omnichannel experiences as quickly as possible. This necessitates a more agile and open architectural approach to technology – one built on microservices and leverages APIs so that new applications and experiences can be built, integrated and delivered faster.

Automation and data-driven retailing will be table stakes

In order for retailers to blend what they’re offering in the store with digital experiences more efficiently, they will also need to automate more. For example, with automation and business intelligence, grocers can take labor that might have been tied up with tender operations and checkout and redistribute those resources to restocking shelves, curbside pick-up or improving customer experiences. 

Automation and access to real-time in-store inventory & supply chain data can also help grocers avoid the supply chain challenges seen in the early days of COVID-19. Grocers will need to find ways to leverage automation to ingest, organize, and analyze data from physical store networks, digital channels, distribution centers to better forecast demand and manage future fluctuations.

How NCR and Google Cloud are helping grocers adapt to disruption with operational agility

Helping grocers improve operational agility to address changing consumer shopping habits and to thrive during times of disruption is something that NCR and Google Cloud have teamed up to do. NCR has over 135 years of experience in retail, having invented the cash register and are continuing to help grocers innovate. NCR Emerald builds upon the company’s leadership in POS software and has turned it into a unified platform that helps grocers operate the entire store from front to back. The solution supports cashier-led checkout, self-checkout, integrated payments, merchandising, and enables regional managers and corporate employees access to the analytics and tools needed to optimize loyalty programs and promotions.

NCR has invested in a comprehensive, agile, and API-led retail architecture that lets grocers continually innovate and design new experiences as customers and the industry evolve. By running Emerald on Google Cloud, NCR can offer the solution on a subscription basis, helping grocers lower upfront capital expenditures and ensuring scalability. What’s more, NCR can tap into Google Cloud’s strength in data, analytics, and openness to deliver three key imperatives. Let’s take a look at each of these below.

Run the way grocers need to while leveraging Google Cloud as a single source of logic

Traditionally the POS system lived in the store. If disaster strikes, people still need access to food and essentials so the grocery store still needs to operate. It hardly gets more mission-critical than that. NCR Emerald is built on microservices, leveraging Kubernetes for front-of-house compute, and VMs (See graphic 1 below). This makes it easy to support lightweight clients accessible by store employees via any range of mobile devices, computer terminals, self-service kiosks, peripheral devices like receipt printers as well as legacy applications.

What’s unique is that because Emerald runs on Google Cloud, it supports all those in-store and digital touchpoints mentioned above, but also allows grocers to run lean. Emerald leverages Google Cloud as a single source of truth and operates a lot of what it does out of logic. Every sales transaction coming from every channel, including e-commerce, can be logged via NCR’s Hosted Service and centralized in BigQuery and Bigtable as a transaction data master. This enables the grocer to manage any transactional use case very consistently, whether it e supporting customers who want to purchase in one store and return in another, offering digital receipts or the ability to exchange online purchases in store. Emerald on Google Cloud can help retailers extend capabilities through the power of the cloud but not need to live exclusively in the cloud. In other words, the solution allows grocers the ability to run the way they need to.

Enable data-driven and real-time decision making for grocers

Store managers, regional managers, category managers, and others all require different cuts of the data to do their jobs effectively. However, data silos persist and how data is formatted and arranged can still remain pretty static. Therefore allowing users with different roles the ability to view and analyze that data quickly and in different ways continues to be a challenge. 

As mentioned above, Emerald leverages Google Cloud data management solutions as the central repository for transactional, behavioral, and merchandising data. Every transaction from every store and every channel can be stored via NCR Hosted Service on BigQuery and Bigtable. NCR Analytics then harnesses the advanced analytical and data visualization capabilities of Looker to help grocers get a consolidated view of their business across all channels and then allow employees to slice and dice the data they way they need to. NCR Analytics also leverages the power of Google Cloud AI and machine learning to add another level of intelligence to the retailer’s data. For example, store managers can visualize how well they’re using their real estate and see how productive lanes 1-3 are compared with 7-10 or compare self-service versus manned lanes. By mapping to the retailer’s own catalog, they can also break down category-level performance and trends.

NCR Analytics takes advantage of Google Cloud’s data pipeline to reduce processing time, with scaling and resource management provided out of the box. By letting the cloud store and process the data, NCR is providing the ability for retailers to analyze their data in near real-time across all platforms – a real game changer in the grocery business.

Open APIs let grocers continually enrich the retail experience

Finally, Emerald is built on an API-first architecture managed through Apigee. It uses the power of Apigee as an open API platform to expose how Emerald can work with other NCR applications like loyalty and promotions, and third party applications like mobile ordering and order delivery to enrich the grocery experience for employees and customers. Every API that Emerald uses is available on Apigee, allowing them to share code samples and giving developers the ability to run scripts. This approach can allow retailers the ability to innovate in a fraction of the time and cost, speeding up 3rd party integrations up front and as businesses grow. 

Take, for example, Northgate Market, a chain of 40 stores in California, that were able to transform its digital operations and enable experiences that set it apart from competitors – quickly and simply with Emerald. It took less than 6 months to go from contract to live deployment in the first store. Since then, Northgate Market has been able to extend their intelligence by leveraging the power of Looker and NCR Analytics.

Learn more about how NCR has been able to leverage an open, cloud-enabled architecture to help customers innovate across the retail, hospitality, and banking industries on the webinar “Role of APIs in Digital Transformation”. You can also learn more about how Northgate uses e-commerce to transform customer experience and gain consumer insights.

For more than 15 years, developers have used Google Maps Platform to deliver location-based experiences to their end users and used location intelligence to optimize their businesses. Along this journey, we’ve made a variety of changes to better support our community as needs have changed and new industries and technologies have emerged. We started rolling out a new website experience, at https://mapsplatform.google.com, to help you better understand the products and solutions best suited to address your objectives. Plus, now you can directly connect to the developer documentation for each product to get started quickly, and you can visualize usage and associated costs to have a better idea of what to expect before getting started. 

Getting to your solution faster 

Maps, Routes, Places are building blocks that let you develop implementations for any use case. Building for specific use cases, however, typically requires using a combination of APIs and SDKs. To help you quickly understand what’s possible and what you need to build for your use case, you can now visit the solutions tab to select from a list of popular use cases or industries. Once you’ve selected a use case or industry, you’re taken to a page where you can explore relevant products, read helpful blog posts, see how other customers have deployed for similar use cases, and more.  

Direct access to developer documentation

Did you know there are more than a thousand pages of developer documentation created to help you get started, unblock you when you’re stuck, and share best practices? Now when you explore a product or solution from the Google Maps Platform website, you can easily navigate back and forth between our website and documentation. Just tap on JS, iOS, Android or API under the product name to get to the documentation you need. 

Budgeting for your project

To help you calculate pricing for your project, we’ve introduced a new pricing calculator. Once you find the product and API or SDK you plan to use, pull the slider to reflect your estimated number of monthly requests. This will automatically update the “monthly cost” column for each product and API or SDK you plan to use. If your estimated monthly requests exceed the slider limit, contact our sales team to ​​learn about volume discounts that start at 20% off. 

We hope our new website makes it easier to discover our products and solutions, estimate your budget, and start building with our documentation so you can deliver helpful experiences to your users and optimize your business. 

For more information on Google Maps Platform, visit https://mapsplatform.google.com.

Many machine learning (ML) use cases, like fraud detection, ad targeting, and recommendation engines, require near real-time predictions. The performance of these predictions is heavily dependent on access to the most up-to-date data, with delays of even a few seconds making all the difference. But it’s difficult to set up the infrastructure needed to support high-throughput updates and low-latency retrieval of data.

Starting this month, Vertex AI Matching Engine and Feature Store will support real-time Streaming Ingestion as Preview features. With Streaming Ingestion for Matching Engine, a fully managed vector database for vector similarity search, items in an index are updated continuously and reflected in similarity search results immediately. With Streaming Ingestion for Feature Store, you can retrieve the latest feature values with low latency for highly accurate predictions, and extract real-time datasets for training.

For example, Digits is taking advantage of Vertex AI Matching Engine Streaming Ingestion to help power their product, Boost, a tool that saves accountants time by automating manual quality control work.“Vertex AI Matching Engine Streaming Ingestion has been key to Digits Boost being able to deliver features and analysis in real-time. Before Matching Engine, transactions were classified on a 24 hour batch schedule, but now with Matching Engine Streaming Ingestion, we can perform near real time incremental indexing – activities like inserting, updating or deleting embeddings on an existing index, which helped us speed up the process. Now feedback to customers is immediate, and we can handle more transactions, more quickly,” said Hannes Hapke, Machine Learning Engineer at Digits.

This blog post covers how these new features can improve predictions and enable near real-time use cases, such as recommendations, content personalization, and cybersecurity monitoring.

Streaming Ingestion enables real-time AI

As organizations recognize the potential business impact of better predictions based on up-to-date data, more real-time AI use cases are being implemented. Here are some examples:

• Real-time recommendations and a real-time marketplace: By adding Streaming Ingestion to their existing Matching Engine-based product recommendations, Mercari is creating a real-time marketplace where users can browse products based on their specific interests, and where results are updated instantly when sellers add new products. Once it’s fully implemented, the experience will be like visiting an early-morning farmer’s market, with fresh food being brought in as you shop. By combining Streaming Ingestion with Matching Engine’s filtering capability, Mercari can specify whether or not an item should be included in the search results, based on tags such as “online/offline” or “instock/nostock.”

• Large-scale personalized content streaming: For any stream of content representable with feature vectors (including text, images, or documents), you can design pub-sub channels to pick up valuable content for each subscriber’s specific interests. Because Matching Engine is scalable (i.e., it can process millions of queries each second), you can support millions of online subscribers for content streaming, serving a wide variety of topics that are changing dynamically. With Matching Engine’s filtering capability, you also have real-time control over what content should be included, by assigning tags such as “explicit” or “spam” to each object. You can use Feature Store as a central repository for storing and serving the feature vectors of the contents in near real time.

• Monitoring: Content streaming can also be used for monitoring events or signals from IT infrastructure, IoT devices, manufacturing production lines, and security systems, among other commercial use cases. For example, you can extract signals from millions of sensors and devices and represent them as feature vectors. Matching Engine can be used to continuously update a list of “the top 100 devices with possible defective signals,” or “top 100 sensor events with outliers,” all in near real time.

• Threat/spam detection: If you are monitoring signals from security threat signatures or spam activity patterns, you can use Matching Engine to instantly identify possible attacks from millions of monitoring points. In contrast, security threat identification based on batch processing often involves potentially significant lag, leaving the company vulnerable. With real-time data, your models are better able to catch threats or spams as they happen in your enterprise network, web services, online games, etc.

Implementing streaming use cases

Let’s take a closer look at how you can implement some of these use cases.

Real-time recommendations for retail

Mercari built a feature extraction pipeline with Streaming Ingestion.

The feature extraction pipeline is defined with Vertex AI Pipelines, and is periodically invoked by Cloud Scheduler and Cloud Functions to initiate the following process:

1. Get item data: The pipeline issues a query to fetch the updated item data from BigQuery.
2. Extract feature vector: The pipeline runs predictions on the data with the word2vec model to extract feature vectors.
3. Update index: The pipeline calls Matching Engine APIs to add the feature vectors to the vector index. The vectors are also saved to Cloud Bigtable (and can be replaced with Feature Store in the future).

“We have been evaluating the Matching Engine Streaming Ingestion and couldn’t believe the super short latency of the index update for the first time. We would like to introduce the functionality to our production service as soon as it becomes GA, ” said Nogami Wakana, Software Engineer at Souzoh (a Mercari group company).

This architecture design can be also applied to any retail businesses that need real-time updates for product recommendations.

Ad targeting

Ad recommender systems benefit significantly from real-time features and item matching with the most up-to-date information. Let’s see how Vertex AI can help build a real-time ad targeting system.

The first step is generating a set of candidates from the ad corpus. This is challenging because you must generate relevant candidates in milliseconds and ensure they are up to date. Here you can use Vertex AI Matching Engine to perform low-latency vector similarity matching, generate suitable candidates, and use Streaming Ingestion to ensure that your index is up-to-date with the latest ads.

Next is reranking the candidate selection using a machine learning model to ensure that you have a relevant order of ad candidates. For the model to use the latest data, you can use Feature Store Streaming Ingestion to import the latest features and use online serving to serve feature values at low latency to improve accuracy.

After reranking the ads candidates, you can apply final optimizations, such as applying the latest business logic. You can implement the optimization step using a Cloud Function or Cloud Run.

What’s Next?

Interested? The documents for Streaming Ingestion are available and you can try it out now. Using the new feature is easy: For example, when you create an index on Matching Engine with the REST API, you can specify the indexUpdateMethod attribute as STREAM_UPDATE.

{
    displayName: "'${DISPLAY_NAME}'", 
    description: "'${DISPLAY_NAME}'",
    metadata: {
       contentsDeltaUri: "'${INPUT_GCS_DIR}'", 
       config: {
          dimensions: "'${DIMENSIONS}'",
          approximateNeighborsCount: 150,
          distanceMeasureType: "DOT_PRODUCT_DISTANCE",
          algorithmConfig: {treeAhConfig: {leafNodeEmbeddingCount: 10000, leafNodesToSearchPercent: 20}}
       },
    },
    indexUpdateMethod: "STREAM_UPDATE"
}

After deploying the index, you can update or rebuild the index (feature vectors) with the following format. If the data point ID exists in the index, the data point is updated, otherwise, a new data point is inserted.

{
    
datapoints: [
        
{datapoint_id: "'${DATAPOINT_ID_1}'", feature_vector: [...]}, 
        {datapoint_id: "'${DATAPOINT_ID_2}'", feature_vector: [...]}
    
]
}

It can handle the data point insertion/update at high throughput with low latency. The new data point values will be applied in any new queries within a few seconds or milliseconds (the latency varies depending on the various conditions).

The Streaming Ingestion is a powerful functionality and very easy to use. No need to build and operate your own streaming data pipeline for real-time indexing and storage. Yet, it adds significant value to your business with its real-time responsiveness.

To learn more, take a look at the following blog posts for learning Matching Engine and Feature Store concepts and use cases:

• Vertex Matching Engine: Blazing fast and massively scalable nearest neighbor search
• Find anything blazingly fast with Google’s vector search technology
• Kickstart your organization’s ML application development flywheel with the Vertex Feature Store
• Meet AI’s multitool: Vector embeddings