Dr. Elliot Smith cannot be accused of lacking ambition. A high achiever with a Ph.D. in Electrical Engineering and a specialist in magnetic resonance imaging (MRI) systems, Smith aims to deliver top quality healthcare to anyone in the world — regardless of their location or wealth.

Dr. Smith has already made strides on this journey with his Brisbane, Queensland-headquartered business, Maxwell MRI. “I saw there was a big gap in the market around automating the diagnosis of health conditions,” he says. “Existing processes were typically manual and involved a lot of people.”

Artificial intelligence (AI) and machine learning can remove a key obstacle to scaling out medicine and improve the efficiency and accuracy of diagnosing conditions, the healthcare entrepreneur believes.

“Our grand vision is to build an AI doctor that anyone can receive affordable support from and connect to in order to obtain results,” explains Dr. Smith.

Maxwell MRI presently enables clinicians to submit anonymised MRI scans to a machine learning enabled AI platform to help diagnose prostate cancer. The service is sold to clinicians who can then charge a per-session fee to clients. As well as obtaining results for individual cases, the MRI scans and associated information is used to ‘train’ the platform to deliver accurate diagnoses faster and in a more affordable way than existing systems do.

Dr. Smith and his team started by running a number of functions and processes on a single server with graphics processing units (GPUs) and sizable hard disk capacity. However, this infrastructure could not scale to support the planned growth of the business. Each case Maxwell MRI processes involves about 200MB of data in MRI scans alone. Once supplementary data, blood test result, pathology results and genetic information is included, this load can reach more than 1GB of data per patient.

The business aimed to process 150,000 cases by the end of 2018. This required a service that could deliver massive scale in data storage and compute, and could easily be accessed from any location. “We wanted to move from three GPUs to 30 GPUs without having to buy more servers or other associated equipment, so the cloud was the natural next step,” says Dr. Smith.

Maxwell MRI evaluated Google Cloud Platform (GCP) and determined that the managed services component of GCP would remove the burden of infrastructure deployment and administration. In addition, Google Cloud Machine Learning Engine would enable the business to scale to as many GPUs as needed to meet demand.

Maxwell MRI started with some small experiments to determine that GCP met all its requirements and completed its migration to the platform in February 2017. “We really started to scale up the data we had and consequently our computing requirements at that time,” Dr. Smith says.

The Maxwell MRI platform features an upload service that enables clinicians to upload imaging and associated data. This service triggers several different upload pipelines that clean and standardise data. They then write imaging data to Google Cloud Storage, and more structured data to a combination of Google Cloud Datastore and Google Cloud Spanner.

The platform then converts the information into records that can be used to ‘train’ new machine learning configurations or run evaluations through existing machine learning pipelines.

“The tasks we perform including segmenting various anatomical regions for analysis and sending those results back into Google Cloud Storage,” says Dr. Smith. “This then commences that repeated process of running Google Cloud Dataflow pipelines and machine learning algorithms, and presenting those outcomes back to the clinicians.”

Existing Literature Validated

The data processed and analysed to date has, Dr Smith says, enabled Maxwell MRI to help validate existing literature that indicates clinicians lack confidence in existing early-stage testing procedures for prostate cancer. This prompts them to move quickly to the biopsy stage to assure themselves their diagnosis is valid. “New technologies have a lot of potential to rectify this situation and guide treatment to be more accurate, specific and cost-effective,” he says.

Results Delivered in 10-15 Minutes

More specifically, using GCP has enabled Maxwell MRI to guarantee to clinicians that results will be delivered within minutes. “Clinicians are used to getting results back in two days to a week,” says Dr. Smith. “We’re saying that with our platform running on GCP we’ll deliver you results in 10 to 15 minutes, regardless of the number of patients coming in.”

Running on GCP has enabled the business to accelerate its development cycles, test new ideas easily on a subset of data, test in parallel and deliver new services considerably faster than in another environment. In addition, the flexible GCP charging model aligned with the ability to scale compute capabilities quickly and easily has enabled the fledgling business to control its costs.

Google technologies are poised to play an integral role in the business’s future. “With Google available, it doesn’t make sense for us to use our own infrastructure,” Dr. Smith says. “Our expertise in AI, machine learning and clinical engagement complements cloud platform specialties of infrastructure, managed services and ease of use. We see a bright future ahead in helping to transform healthcare globally.”

Siemens aims to shape the future—and has the scale, culture, and know-how to realize its ambitions. The Germany-headquartered industrial manufacturing business operates in more than 120 countries and focuses on electrification, automation, and digitalization. Siemens’ extensive portfolio includes industrial production digitalization and automation technologies, diagnostic and therapeutic imaging for healthcare, and building automation and management technologies.

“All our solutions are united by the fact they impact the way people live their lives,” says Stephanie Morton, Siemens’ Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management.

“‘Engineered for life’ is one of our brand claims.”

Hiring “future makers”

Hiring thinkers, dreamers, and doers hungry to transform businesses, industries, and lives is key to Siemens’ success. The business calls its people “future makers” and employs more than 370,000 of them worldwide.

Recruiting for a workforce this size is no easy task. Siemens’ global jobs and careers website has about 5,000 open positions at any one time, and the organization receives 2 million applications to fill 35,000 positions per year.

However, by 2017, the website was experiencing problems that hampered the talent acquisition team’s efforts to fill roles promptly with the right candidates. Its standard keyword matching technology could not optimize the results provided to job seekers, frustrating potential candidates and increasing the workload for recruiters.

“With Cloud Talent Solution, we saw a 30 percent uplift in candidate conversions from search to application.”

—Stephanie Morton, Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management, Siemens

“We found the language we used about our jobs internally was often not the same language job seekers used,” explains Morton. “For example, we may have an engineering position open for our MindSphere cloud-based IoT operating system. We would post this on our jobs and careers website as ‘MindSphere engineer.’ Unfortunately, a job seeker using more general terms such as ‘IoT engineer’ may miss this advertisement.”

In addition, without intelligence and context, the keyword matching technology could not determine job seeker search intentions from misspelled terms. This became more of a problem as job seekers increasingly used mobile devices to conduct searches.

With keyword searches also generating pages and pages of results, job seekers often succumbed to the temptation to apply for every position returned. This left the talent acquisition team drowning in messages and applications.

“We experienced issues around seniority as well—for example, people looking for senior legal roles were receiving irrelevant results for internships or junior roles,” says Morton.

Change needed

These problems could not persist for a team charged with continuously improving candidate experiences. The team opened discussions with Jibe, a recruitment platform provider that operated Siemens’ external career websites. Jibe had collaborated with Google to integrate Cloud Talent Solution—a solution that uses machine learning to better understand job content and job seeker intent—into its platform.” Jibe offered the integrated solution to Siemens as one of its most forward-thinking customers,” says Morton.

Morton’s team reviewed Cloud Talent Solution and was immediately excited by its potential to quickly make a profound difference in its recruitment activities—without requiring recruiters and other team members to invest considerable time and effort. Cloud Talent Solution could enable the website to understand the broad intent of a job seeker—including synonymous positions—and deliver considerably more relevant results.

“We can authoritatively say Cloud Talent Solution is improving the experience for job seekers; they are conducting more searches, those searches are more effective, and more of them are converting to job applications.”

—Stephanie Morton, Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management, Siemens

After Cloud Talent Solution expanded to encompass more than 100 languages—enabling Siemens to offer a consistent experience to candidates from a range of countries and backgrounds—Morton’s team gave Jibe and Google the green light to proceed.

“We had two key issues to address during the implementation,” says Morton. “The first was to make sure Google Cloud was aware of any Siemens-specific recruitment terms. For example, we have German-language posts that include terms commonly used internally regarding internships and junior positions. We worked closely with our partners to make sure these terms were represented in the algorithm to complement the vast constellation of already-mapped job titles in Cloud Talent Solution.”

Siemens also needed to quantify Cloud Talent Solution benefits to sell the service internally within the business and win support to proceed beyond a pilot. “Because Cloud Talent Solution was effectively an invisible layer behind our website, we had to test its impact,” says Morton.

A 30 percent uplift

Working with Jibe, Siemens conducted A/B testing. Five percent of candidates in the test group used the keyword-based search experience and 95 percent of candidates the search experience powered by Cloud Talent Solution. The testing accommodated changes in candidate queries over time and context.

“With Cloud Talent Solution, we saw a 30 percent uplift in conversions from search to application,” says Morton.

“We can authoritatively say Cloud Talent Solution is improving the experience for job seekers; they are conducting more searches, those searches are more effective, and more of them are converting to job applications,” she adds. “Anecdotally, recruiters are seeing fewer applications from job seekers who have applied to long lists of positions returned from search queries.”

“Partnering with market-leading providers like Google Cloud can help us stay one step ahead of the competition over the journey.”

—Stephanie Morton, Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management, Siemens

In the longer term, Siemens plans to monitor the impact of Cloud Talent Solution on metrics such as candidate retention rates and manager satisfaction.

With Cloud Talent Solution well established within the business, Siemens is now working with Google Cloud to add new features—including voice assistants—to enhance the candidate experience. “Helping candidates laser in on the right positions and make successful connections is something we’re doing everything we can to make happen,” says Morton.

More broadly, Cloud Talent Solution and Jibe are helping Siemens’ talent acquisition and the rest of its human resources function build its reputation and execute its strategy. “We are becoming a function driven by delivering amazing technology-based solutions to our employees and our candidates,” says Morton. “People within Siemens are very impressed to see human resources leading the way in working with Google Cloud to deliver a solution like this at a global scale.”

“The more light-touch exercises like this we can do, the fewer big, expensive, time-consuming initiatives we need to take,” she says. “Furthermore, partnering with market-leading providers like Google Cloud can help us stay one step ahead of the competition over the journey.”

We’re partnering with Coursera, one of the largest online learning platforms in the world, on a new ML Academy to help you sharpen your machine learning (ML) skills and learn about the latest ML technologies from Google Cloud at no-cost. The academy has three core components for you to take advantage of in July and August:

Join the ML Academy webinar to get started

Our July 22 webinar will kick off the ML Academy. During the webinar, Audrey Holmes, Senior Data Scientist at Coursera, will discuss the current market for individuals with ML skills. We will discuss key challenges ML practitioners are facing and how Google Cloud’s products and solutions are addressing these challenges. Doug Kelly, Google Cloud’s Head of AI Learning Services Portfolio, will discuss the best ways to gain ML skills and share a demo of how to train and serve a custom TensorFlow model on Vertex AI, Google Cloud’s machine learning model training and development platform. You’ll have an opportunity to ask questions throughout the webinar. 

After the webinar, you’ll receive an email with a one month free* offer from Coursera for the new Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate. Can’t attend the webinar live? You can watch the webinar on-demand after July 22 and the offer can be claimed until August 31, 2021. 

Keep growing your ML skills with Coursera

There are nine courses in the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate. The courses will cover ML fundamentals, introduction to TensorFlow, production ML systems, and more to help you prepare for the Google Cloud Professional Machine Learning Engineer certification exam. The courses will include labs so you can apply the concepts you’re learning and gain real world experience. 

Demonstrate your skills with badges 

To access even more labs and gain skill badges along the way to demonstrate your knowledge, sign up for the skills challenge. When you select the ML and AI skills challenge track, you’ll get 30 days free access to labs that will walk you through BigQuery, AI Platform, ML APIs, Explainable AI, and more. 

To earn a skill badge, you’ll need to complete the labs and take a final assessment to test your skills. You’ll have the chance to earn three skill badges through the challenge: Perform Foundational Data, ML, and AI tasks; Integrate with Machine Learning APIs; and Explore Machine Learning Models with Explainable AI. 

Ready to join the ML Academy? Sign up for the July 22 webinar here to get started. 

^{*This offer is only available for those who have never previously paid for Coursera. A credit card is required to activate your first month free. After the first month is over, your subscription will auto-renew to a $49 monthly charge until you cancel your Coursera subscription.}

Google Cloud results

• Helps enable instant document approval with optical character recognition by Vision API
• Processes 100,000 documents in 20 minutes with automated document processing product Recognic, powered by Vision API and Apigee
• Helps increase the number of applications processed by 30% within the same timeframe

The insurance landscape in India has seen significant changes in recent years with the adoption of new technology. As one of the major insurance providers in the country, ICICI Prudential Life Insurance has aimed to lead in this transformation journey. “There has been a data explosion across India over the past few years, together with a high mobile penetration rate. Today, about 60% of our customers approach us via mobile, for example, which was certainly not the case before,” says Alpesh Karnik, SVP, IT, at ICICI Life Insurance.

Consumer expectations have also evolved, with easier access to information and online services. “Consumers today are more informed on the importance of investing in insurance products, so there’s much more of a pull factor when it comes to sales, but they also want to be able to get these products quickly and easily,” adds Alpesh. To meet the demands of these consumers, ICICI Prudential Life Insurance realized it needed to make its processes even faster and more efficient. Looking to upgrade its infrastructure, the company turned to Google Cloud.

“The biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others.”—Alpesh Karnik, SVP, IT, ICICI Prudential Life Insurance

Serving customers better by speeding up processes with Google Cloud

ICICI Prudential Life Insurance’s distributors were already using tablets to input customer data faster and more efficiently, but many of the company’s solutions still required a team at the back end to manually sift through documents for approval. This meant that customers needed to wait five or six hours, or sometimes until the next working day, to know if their documents were approved or needed revision.

That all changed after partnering with Google Cloud Premier Partner Searce to take advantage of its AI/ML powered automated document processing product Recognic, which is built on Google Cloud. Developed using the optical character recognition (OCR) capabilities of Cloud Vision, Recognic reads, understands, and validates documents at scale, enabling organizations that handle massive amounts of paperwork to digitize these documents and then accurately store and index them.

“Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources.”—Alpesh Karnik, SVP, IT, ICICI Prudential Life Insurance

“In the case of ICICI Prudential, the biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others,” Alpesh adds.

Alpesh explains that if the details on the application form match the documents provided, the case doesn’t need to go to the underwriter for further checks and can go directly to policy issuance. “Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources.”

ICICI Prudential Life Insurance is also working with Searce to build deep learning models into Recognic so that it can overcome template barriers and input data from a variety of forms. This is particularly helpful for financial and medical documents underwriting because unlike a passport or driving license, financial documents have a higher structural complexity.

As customer data becomes more important in the work of ICICI Prudential Life Insurance, so does protecting it, and the company is taking every measure to safeguard the security and privacy of its customers’ information. “Details of customers’ contactability are automatically removed by Google Cloud after processing is complete. This step in the workflow gives us the confidence that data is not stored at any level of the optical character recognition process,” says Alpesh.

Partnering with the right teams for dedicated support

In achieving the best solution for its business goals, ICICI Prudential Life Insurance recognizes the importance of its decision to work with partners that truly understand the insurance business. “There are many intricacies involved in this business, and it’s clear that both Google Cloud and Searce really took the time to understand our underwriting processes before coming up with a solution,” says Alpesh. He adds that during the implementation process, all findings were well documented and queries were responded to quickly.

“We didn’t want to take any shortcuts deploying Recognic, but at the same time, we didn’t want to draw out the implementation process. The excellent support from both Google Cloud and Searce throughout the journey was reassuring for us as they were always thinking ahead.”

Future-proofing the organization through machine learning and AI

In the coming years, Alpesh foresees the insurance industry to be even more agile than it is today. “I doubt elaborate processes such as underwriting or operations checks will need to be done manually in the future. Everything will be done through machine learning and AI.” In light of this, ICICI Prudential Life Insurance is doing everything it can to prepare, as customers’ expectations are set to keep evolving. “We have to be prepared for the future, and I believe that with Google Cloud, we can do it.”

About ICICI Prudential Life Insurance

ICICI Prudential Life Insurance aims to lead the Indian insurance field through quality products and a hassle-free claim settlement experience. A customer-centric company, it offers long-term savings and protection plans to meet customers’ needs at every stage of life.Industries: Financial Services & InsuranceLocation: India

About Searce

Searce is a niche cloud consulting business with futuristic tech in its DNA, focused on “realizing the Next in the Now” for its clients. Specializing in cloud data engineering, AI/ML, and ad

Not long ago, building AI into recommendation engines was a daunting, expensive task that could take years to get off the ground. But as Bazaarvoice has shown, with the help of cloud services, the time from AI investment to business outcomes is shorter than ever. 

Bazaarvoice is the leading provider of product reviews and user-generated content (UGC) solutions that help brands and retailers understand and better serve customers. Its 2019 acquisition of Influenster.com, a community of consumer reviewers 6.5 million strong, expanded the Bazaarvoice portfolio with a platform where consumers can share their candid opinions — and share they have, over 54 million times. 

After the acquisition, Bazaarvoice expanded the site’s product diversity by 53%, to more than 5.4 million unique products. To keep user engagement high, Influenster must be seen as both a source of trusted, transparent reviews and a place for customers to discover useful, relevant products for the first time. By introducing shoppers to new products Influenster not only provides value to customers but also helps brands collect consumer insights. 

Influenster started out as a place where people gathered to share their honest thoughts on beauty products but quickly expanded to nearly every category, from Art to Wearables. Because of the much smaller scope, the site started and flourished under a rules-based recommendation engine. However, as Influenster expanded its scope under Bazaarvoice, a more robust recommendation system became necessary. In its earliest days, Influenster was successful because of the human perspective it offered: For every product there was a litany of reviews and images that made users feel as if they were getting an endorsement on a product from a friend. 

The Bazaarvoice engineering team asked themselves how they could keep that same feeling of personalization with an ever-growing catalog of items and categories. They needed recommendations that could scale with the site, rather than requiring more rules be constructed each time a new product category was introduced. They also needed to ensure the Influenster experience would remain performant even towards unknown members.

Bazaarvoice tested out several recommendation engines, benchmarking each against their current rules-based system. In the end they decided on Google Cloud’s Recommendations AI because of its transparent billing, ease of integration and setup, and naturally, its proven results.

Transparent Billing

“Part of what the engineers loved was they knew exactly what it was going to cost as it scaled” says Nick Shiftan, SVP, Content Acquisition Services Product Unit for Influenster. The goal was to build once and innovate rather than leave a wake of technical debt only to be tackled when costs grew unexpectedly out of control. Google Cloud’s straightforward and pay-as-you-go billing allowed them to anticipate how costs would grow as user interactions did and plan accordingly.

Ease of integration

“I’m positively surprised how Google packed such a complex system in a very easy-to-use API” remarks Eralp Bayraktar, the Software Engineering team lead overseeing the project. Because the original team was made of just one full-time engineer the ease of integration became an even more critical feature. Not only does Recommendations AI pull from years of suggestion expertise in Google Search and YouTube, but in combination with Ad’s Merchant Center, it also creates a streamlined process for importing product metadata. From there, creating a model becomes a matter of picking the preferred recommendation type and then the business objective to optimize for. Once the model is created and the API integrated into the website, the code is already deployed at the global scale: There are no further architectural considerations to ensure recommendations are available to users worldwide. For Bazaarvoice, this meant going from ideation to production in one month.

Proven Results

“We have used it for product recommendations and off-loaded our DB-tiring business logic to Recommendations AI, which resulted in overall faster response times and much better recommendations as proven by our A/B tests,” Eralp continues.  

Bazaarvoice began by A/B testing Recommendations AI against their rules-based system. Early on in the experimental phase they noticed a clear and consistent 60% increase in the click-through rate over their original recommendation system. 

Even more impressive was the performance on Unknown Members. For every person that signs up for an account on Influenster.com there are many other visitors that come to the website and leave without fully registering. This is typically referred to as the “cold start” problem in the industry — how do you figure out what to recommend to those people without their history, behavior, or preferences? Recommendations AI gives you the option to input and train on unknown users, and by providing metadata on products, it can provide high-quality suggestions to registered members and first-time users alike. 

With a mind to the future, Eralp concludes his thoughts on Bazaarvoice’s experience: “It enables discovery by adding an adjustable percentage of cross-category products [for] healthier [traffic distribution] across all our catalog. We are investing in data science and having the Recommendations AI as the baseline is a good challenge for us to thrive.” 

To learn more about Recommendations AI and how it can help your organization thrive, check out our recently published 4 part guide which kicks off with an overview on “How to get better retail recommendations with Recommendations AI.” This series also covers data ingestion, modeling, as well as serving predictions & evaluating Recommendations AI. You can also easily get started with our Quickstart Guide.