During uncertain times, ecosystem partnerships that leverage APIs have proven to help companies scale and address gaps in their businesses. Apigee customers, like CHAMP Cargosystems, have pursued API-first ecosystem models to enter adjacent markets, create new customer interaction models, and rapidly grow their brand reach and partner ecosystems. As a result of building their API ecosystem, they are transacting 300 million electronic exchanges and 20 million shipments per year.

CHAMP selected Apigee to provide an API platform and developer self-service portal option to all of its SaaS customers. Google Cloud’s Apigee API management platform and portal allows CHAMP and its customers to quickly connect to a variety of backend systems, including in-house, third party apps, marketplace portals and more– thereby accelerating their digitization strategies and opening up new markets through an API ecosystem.

Join this webcast and hear from this leading Enterprise company on how to:

• Identify new revenue sources and markets using an API management platform
• Improve time to market while still complying with all industry requirements
• How to create a proof of concept to grow API adoption throughout your organization
• Align internal business leaders and partners to see the importance of an API-first platform vision

Digitec Galaxus AG is the biggest online retailer in Switzerland, operating two online stores: Digitec, Switzerland’s online market leader for consumer electronics and media products, and Galaxus, the largest Swiss online shop with a steadily growing range of consistently low-priced products for almost all daily needs. 

Known for its efficient, personalized shopping experiences, it’s clear that Digitec Galaxus understands what it takes to deliver a platform that is interesting and relevant to customers every time they shop. 

The problem: Personalizing decisions for every situation

Digitec Galaxus already had established an engine to help them personalize experiences for shoppers when they reached out to Google Cloud. They had multiple recommendation systems in place and were also extensive early adopters of Recommendations AI, which already enabled them to offer personalized content in places like their homepages, product detail pages, and their newsletter. 

But those same systems sometimes made it difficult to understand how best to combine and optimize to create the most personalized experiences for their shoppers. Their requirements were threefold:

1. Personalization: They have over 12 recommenders they can display on the app, however they would like to contextualize this and choose different recommenders (which in turn select the items) for different users. Furthermore they would like to exploit existing trends as well as experiment with new ones.
2. Latency: They would like to ensure that the solution is architected so that the ranked list of recommenders can be retrieved with sub 50 ms latency.
3. End-to-end easy to maintain & generalizable/modular architecture: Digitec wanted the solution to be architected using an easy to maintain, open source stack, complete with all MLops capabilities required to train and use contextual bandits models. It was also important to them that it is built in a modular fashion such that it can be adapted easily to other use cases which have in mind such as recommendations on the homepage, Smartags and more . 

To improve, they asked us to help them implement a machine learning (ML) contextual bandit based recommender system on Google Cloud taking all the above factors into consideration to take their personalization to the next level. 

Contextual bandits algorithms are a simplified form of reinforcement learning and help aid real-world decision making by factoring in additional information about the visitor (context) to help learn what is most engaging for each individual. They also excel at exploiting trends which work well, as well as exploring new untested trends which can yield potentially even better results. For instance, imagine that you are personalizing a homepage image where you could show a comfy living room couch or pet supplies. 

Without a contextual bandit algorithm, one of these images would be shown to someone at random without considering information you may have observed about them during previous visits. Contextual bandits enable businesses to consider outside context, such as previously visited pages or other purchases, and then observe the final outcome (a click on the image) to help determine what works best. 

Creating a personalization system with contextual bandits

While Digitec Galaxus heavily personalizes their website homepages, they are very very sensitive and also require more cross-team collaboration to update and make changes. 

Together with the Digitec Galaxus team, we decided to narrow the scope and focus on building a contextual bandit personalization system for the newsletter first. The digitec Galaxus team has complete control over newsletter decisions and testing various ML experiments on a newsletter would have less chance of adverse revenue impact than a website homepage. 

The main goal was to architect a system that could be easily ported over to the homepage and other services offered by Digitec with minimal adaptations. It would also need to satisfy the functional and non-functional requirements of the homepage as well as other internal use cases.

Below is a diagram of how the newsletter’s personalization recommendation system works:

• The system is given some context features about the newsletter subscriber such as their purchase history and demographics. Features are sometimes referred to as variables or attributes, and can vary widely depending on what data is being analyzed. 
• The contextual bandit model trains recommendations using those context features and 12 available recommenders (potential actions). 
• The model then calculates which action is most likely to enhance the chance of reward (a user clicking in the newsletter) and also minimize the problem (an unsubscribe). 

Calculating whether a click was a newsletter or an unsubscribe enabled the system to optimize for increasing clicks and avoid showing non-relevant content to the user (click-bait). This enabled Digitec Galaxus to exploit popular trends while also exploring potentially better-performing trends. 

How Google Cloud helps

The newsletter context-driven personalization system was built on Google Cloud architecture using the ML recommendation training and prediction solutions available within our ecosystem. 

Below is a diagram of the high-level architecture used:

The architecture covers three phases of generating context-driven ML predictions, including: 

ML Development: Designing and building the ML models and pipeline 
Vertex Notebooks are used as data science environments for experimentation and prototyping. Notebooks are also used to implement model training, scoring components, and pipelines. The source code is version controlled in Github. A continuous integration (CI) pipeline is set up to automatically run unit tests, build pipeline components, and store the container images to Cloud Container Registry. 

ML Training: Large-scale training and storing of ML models 
The training pipeline is executed on Vertex Pipelines. In essence, the pipeline trains the model using new training data extracted from BigQuery and produces a trained, validated contextual bandit model stored in the model registry. In our system, the model registry is a curated Cloud Storage. 

The training pipeline uses Dataflow for large scale data extraction, validation, processing, and model evaluation, and Vertex Training for large-scale distributed training of the model. AI Platform Pipelines also stores artifacts, the output of training models, produced by the various pipeline steps to Cloud Storage. Information about these artifacts are then stored in an ML metadata database in Cloud SQL. To learn more about how to build a Continuous Training Pipeline, read the documentation guide.

ML Serving: Deploying new algorithms and experiments in production 
The training pipeline uses batch prediction to generate many predictions at once using AI Platform Pipelines, allowing Digitec Galaxus to score large data sets. Once the predictions are produced, they are stored in Cloud Datastore for consumption. The pipeline uses the most recent contextual bandit model in the model registry to evaluate the inference dataset in BigQuery and give a ranked list of the best newsletters for each user, and persist it in Datastore. A Cloud Function is provided as a REST/HTTP endpoint to retrieve the precomputed predictions from Datastore.

All components of the code and architecture are modular and easy to use, which means they can be adapted and tweaked to several other use cases within the company as well.

Better newsletter predictions for millions

The newsletter prediction system was first deployed in production in February, and Digitec Galaxus has been using it to personalize over 2 million newsletters a week for subscribers. The results have been impressive, 50% higher than our baseline. However, the collaboration is still ongoing to improve the results even more. 

“Working at this level in direct exchange with Google’s machine learning experts is a unique opportunity for us. The use of contextual bandits in the targeting of our recommendations enables us to pursue completely new approaches in personalization by also personalizing the delivery of the respective recommender to the user. We have already achieved good results in our newsletter in initial experiments and are now working on extending the approach to the entire newsletter by including more contextual data about the bandits arms. Furthermore, as a next step, we intend to apply the system to our online store as well, in order to provide our users with an even more personalized experience. To build this scalable solution, we are using Google’s open source tools such as TFX and TF Agents, as well as Google Cloud Services such as Compute Engine, Cloud Machine Learning Engine, Kubernetes Engine and Cloud Dataflow.”—Christian Sager, Product Owner, Personalization ( Digitec Galaxus)

Since the existing architecture and system is also dynamic, it will automatically adapt to new behaviours, trends, and users. As a result, Digitec Galaxus plans to re-use the same components and extend the existing system to help them improve the personalization of their homepage and other current use cases they have within the company. Beyond clicks and user engagement, the system’s flexibility also allows for future optimization of other criteria. It’s a very exciting time and we can’t wait to see what they build next!

Hackensack Meridian Health (HMH) executive Mark Eimer explains how an ambitiously-timed rollout of a comprehensive suite of Google products helped the entire organization—from doctors to IT staff—achieve better security, cultivate a more equitable work environment, and ultimately, improve patient outcomes.

How does a recently merged, 17-hospital healthcare system fast-track a platform migration and hardware rollout securely and in a way that improves work for everyone, regardless of location or role? These are the questions that kept me up at night in early 2020, when the pandemic demanded a “big bang”—something our legacy laptops and operating systems couldn’t handle.

We began our work with Google in 2020 with the adoption of Chrome as our default browser. As we migrated platforms, keeping patient data safe was of the utmost importance to us, along with providing every staff member with the tools they needed to work virtually. Our staff often experienced issues accessing our web-based applications using Internet Explorer or Edge Browser, a problem that went away when we switched to Chrome. Chrome’s versatile compatibility also made it easier for my team to migrate all of our web-based operations, and Chrome’s security and manageability were key components to making this switch a huge win for the organization.

The success of this migration led us to extend our Google partnership to patient care applications—where Google’s expertise in AI and ML helps scale the use of diagnostics tools and improve other aspects of the patient journey.

Achieving security at every step 

Like so many other healthcare organizations, we’ve been concerned about ransomware attacks. This is part of why we moved to Google Workspace and distributed over 3,000 Chrome OS devices in kiosk mode in March of 2020, when many of us went remote due to the pandemic. We were very concerned about team members accessing corporate applications through home devices that were running EOL operating systems (WIN7), as well as a general lack of antivirus and encryption measures.

We were protected by the fact that Google’s software and hardware both had built-in security features that we needed to stave off sophisticated attackers. For example, Chrome OS automatically updates to the latest security update and encrypts data living outside the cloud on the hardware. These features protected us from security-related disruptions, letting us securely move a huge library of file shares and emails across thousands of accounts to Google Chrome OS in just four months.

A year later, in March 2021, we migrated the enterprise over to Google Workspace and saw an immediate reduction in spam by 30% from the inherent built-in AI/ML. This meant staff were less likely to receive (and click through) phishing attempts. My team could connect, create, and collaborate easily and securely—even as more of us were working from home and needed to access sensitive data remotely. 

Leveling the playing field

As an organization, we were surprised by how many team members didn’t have personal computers at home. We quickly decided that if we needed team members to work from home, the health network would have to supply hardware. Chromebooks’ lower price tag compared to PCs—on top of their built-in security controls—allowed us to purchase, deploy, and support that initial distribution of 3,000 Chromebooks to team members in less than three weeks, providing devices to every eligible remote employee instead of just a select few. This was vital to reaching our equitable technology goal as part of our diversity and inclusion initiative: everybody has the same tools to do good work.

When all employees have what they need to do their jobs well, we get better patient outcomes. Before we began this cloud adoption journey, patient and staff experiences were different within the hospitals and outside of them. 

Now it’s the same wherever our staff is, and we’ve seen efficiency and accessibility benefits extend to the patient side. For example, we built a web-based contact center that supports 80 locations that use Workspace and Chrome OS devices. Since customer service, admin, and providers are all on the same system, it has become a one-stop shop for patients.

Furthermore, through the Grow with Google program, we were able to provide another benefit to employees that drove our equity goals. Google trained 50 non-IT staff members—from environmental services, food and nutrition, and other non-tech areas who were interested in making a career change to IT—on the Google products we were using. They may not have thought about switching to a career in IT before the Grow with Google program came to our organization, but through this partnership, they now have that opportunity.

A strategic, long-term partner

With any large-scale rollout, the work doesn’t end once laptops are in employee hands. Google has shown their commitment to long-term collaboration as they continuously optimize their products for the unique needs of healthcare providers and go the extra mile in tailoring tools to our staff’s workflows. 

For example, on the Chrome OS side, the Google team has helped our registration desks and document centers with device integration for hardware like credit card readers and e-signature pads. They’ve also helped us meet security and privacy requirements mandated by state and federal governments around HIPAA, Medicaid, and Medicare reimbursements. Over this next year, we’ll look at a feature roadmap with Google Cloud to deliver further enhancements, iterating on the product itself to meet our needs for the present and the future.

Delivering the future of healthcare

The benefits we’ve seen around security and usability—and the ability to provide all staff with equal access to Google’s technology—are why we’re expanding our partnership with Google to both the administrative and clinical sides of HMH. In addition to further Google rollouts with corporate, next year we’re distributing Chromebooks to all 350 of our ambulatory clinics.

We’re also working with the Google professional services team to create a custom AI model that analyzes 3D mammogram images. This AI model will enable two providers to read mammograms—which adheres to international best practices but is currently rare in the US—without requiring additional time. Conducting double readings of mammograms will yield better health outcomes for our patients, such as a lower patient recall rate and an increased accuracy in detecting breast cancer.

We’re currently building the model using a variety of Google Cloud products, including Cloud Healthcare API. Once complete, this model is expected to be trained, deployed, and maintained in Google’s Vertex AI, allowing our providers to be more productive as they make clinical decisions with AI support. As the model is proven over time, we plan to make the predictive services accessible to other healthcare organizations.

With Google, we’re able to achieve a unified architecture for storing data as well as training and deploying AI models, which enable our staff to work more efficiently and securely from anywhere. While I may not be able to predict the future as accurately as AI can, I foresee our continued partnership with Google as a key part of HMH’s improved provider and patient outcomes.