For a business to succeed in the long term, it needs to learn not just to adapt to inevitable change, but to harness it. South Africa-based PPS has been an insurance company since 1941 and today is the biggest mutual insurance provider in the country.

As a mutual company, PPS is owned by more than 200,000 members, making them shareholders. In recent years, PPS and other companies like it have been affected by a number of external factors.

“For one thing, technology platforms have brought in a new gig economy that has all kinds of implications for insurance,” says Avsharn Bachoo, CTO at PPS. “What we’ve been seeing is basically a disruption of the South African insurance industry. We chose to see that as an opportunity.”

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely. To embrace the world of AI and machine learning effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

—Avsharn Bachoo, CTO, PPS

In early 2018, faced with an uncertain economic environment that was squeezing growth and profitability, PPS decided to transform itself from a traditional broker-based business into a digital insurance provider. A key pillar of this new strategy was to overhaul the company’s technology infrastructure. To turn the strategy into reality, Avsharn and his team chose Google Cloud Platform (GCP).

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely,” says Avsharn. “To embrace the world of AI and machine learning (ML) effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

Power, speed, flexibility with Google Cloud Platform

Previously, PPS maintained an on-premises IT infrastructure, which worked for its traditional business but was unsuited for its new way of working. In early 2018, the company started working on new products for its members but this required large amounts of compute power that proved prohibitively expensive with on-premises servers. Even existing products were starting to require more than the infrastructure could deliver. Aging equipment meant that it’s testing and quality assurance environments bore little resemblance to the actual production environment.

“We had no pre-production environments at all,” says Avsharn, resulting in more work for developers after products had been released. Meanwhile, the capital required to buy and configure more servers for new projects meant fewer resources available for innovation, and left the company less able to react to changes in the market. PPS knew it had to find a cloud-based alternative.

Shortly after devising a new digital strategy, PPS engineers attended a training session on cloud infrastructure given by leading South African Google Cloud Partner Siatik. Impressed with the presentation, PPS engaged Siatik to help run a proof of concept for a cloud-based infrastructure, running on GCP. With on-site engineers and constant communication, Siatik formed a very close working relationship with PPS. “The team at Siatik was exemplary,” recalls Avsharn. “They were well-organized, with cutting-edge technical acumen and very creative solutions to our problems. They were real game-changers.”

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information. Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

—Kimoon Kim, Lead Solution Architect and Data Engineer, Siatik

The proof of concept was successful, with GCP outperforming the existing infrastructure in terms of how it handled compute demands, databases, and storage.

“It’s the speed of GCP that really impresses us,” says Avsharn. PPS saw that GCP wasn’t just an opportunity to migrate its existing infrastructure to the cloud. With Siatik’s help, it redesigned its monolithic core architecture to one based around microservices using Google Kubernetes Engine (GKE). For data processing and storage, Cloud Dataflow and Cloud Datastore proved invaluable, while Stackdriver helped the IT team stay on top of logging and monitoring the system.

“Google Cloud makes migrations very easy,” says Brett St. Clair, CEO at Siatik. “It takes care of all the hard work with configurations and replications, so when we switch the machines on, everything is ready and working.”

The ease with which PPS migrated to GCP means that it can now tackle strategic goals much more quickly than before. The most ambitious of these is an AI-powered product recommendation platform. Information is collected from customers who opt in at a defined point in their journey, this database is queried using BigQuery, and the information is fed into the platform. The AI model then calculates the most appropriate products for each member, according to their personal history.

“Most of the product recommendation engines out there are based on clustering, where you’re offered products based on your peer groups,” explains Avsharn. “For the first time, we can make recommendations to members based on their individual preferences and historical behavior. That’s really powerful for us.”

Siatik helped PPS use TensorFlow and Cloud Machine Learning Engine to build the AI platform. For the engineers, these easy-to-use tools helped speed up the process considerably, allowing them to host the models locally without any fuss. Previously, it took one to three months to manually build the model and match an offer to a customer. With the AI platform, a match takes just a few minutes. Cloud ML Engine, in particular, helped the platform adapt to new information on the fly and easily make adjustments to its hyperparameters, that is, preset variables which define the model-training process.

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information,” says Kimoon Kim, Lead Solution Architect and Data Engineer at Siatik. “Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI. We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”

—Avsharn Bachoo, CTO, PPS

Harnessing artificial intelligence for real-world results

PPS deployed its new AI recommendation platform in December, 2018. Just a couple of months later, its impact was clear. “In around eight weeks, we saw a 5 percent growth in sales,” says Avsharn. “It’s been a direct result of building our recommendation platform with Google Cloud. We can offer the right products to the right members.”

For developers and engineers at PPS, working with Google Cloud gives them access to high performance technology and automation options with GKE. As a result, the infrastructure runs 70 percent faster than before with fewer cores and less memory. Developers can also work in mature testing environments, and for the first time, are able to build pre-production environments, leading to better quality products. More strategically, moving to a serverless, cloud-based infrastructure has helped PPS take control of its budget, moving away from intermittent, large capital spends to more manageable, project-to-project flows of operational expenditure. The company expects to see savings of around 50 percent, or $695,000.

“We have a lot more flexibility with our resources thanks to Google Cloud,” says Avsharn. “When we have a new idea, we don’t have to outlay new capital such as servers before we can even start working on it. We just spin up instances when we want and spin them back down when we’re done.”

With the AI platform deployed and working well, PPS is already looking at ways to improve it, including real-time updates and further automation. Soon, the company will integrate the platform with more sales campaigns for more effective targeting to boost sales even further. Meanwhile, it’s also experimenting with machine learning to spot patterns in data at scale for fraud analytics and risk assessment.

For PPS, working with Google Cloud has helped it transform quickly and effectively from disrupted to disruptor. The company is now looking to gain the same transformative effects by implementing G Suite for increased productivity and collaboration.

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI,” says Avsharn. “We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”

Businesses rely on processing an inflow of documents to drive processes and make decisions. Many such documents are combined into a single file. For example, a loan application may have a driver’s license, paystub, W2, bank statement, and other document types within a single file. The complexity of handling many document types within a single file makes it difficult for businesses to manage at scale. 

At Google Cloud, we’re committed to solving these challenges with continued investment in our Document AI solutions suite which offers machine learning products for document processing and insights. Document AI Workbench helps users quickly build ML models with world-class accuracy, trained for their specific use cases. In February 2023, we launched the Custom Document Extractor (CDE) in General Availability (GA) to help users extract structured data from documents in production use cases. In March 2023, we launched the Custom Document Classifier (CDC) in GA to help automatically classify document types. Today, we announce the newest feature of Document AI Workbench, Custom Document Splitter (CDS) in GA to help users automatically split and classify multiple documents within a single file. 

CDS provides tangible business value to customers by helping them sort and classify documents. For example, businesses can validate if they have all the needed documents from an applicant. Furthermore, individually classified documents enable businesses to better automate downstream processes, including selecting the proper storage, analysis, or processing steps based on the document type. The efficiencies enabled by CDS helps businesses lower their document processing time and cost.

Benefits of splitting and classification models in Document AI Workbench 

Document AI Workbench can save time and money by simplifying model training, from dataset management, to testing, to deployment. CDS helps businesses achieve higher automation rates to scale processes while lowering costs.

Sean Earley, VP of Delivery Services at Zencore said, “We completed a project for a large bank using Document AI Workbench to split, classify, and extract data from documents to automate Home Mortgage Disclosure Act reporting. Given the accuracy of the models we built, our client estimated increasing loan reporting coverage from 20% to 100% while eliminating thousands of errors per year, drastically reducing the operational cost of the bank’s compliance reporting procedures.”  

Fabian Beckmann, Manager Artificial Intelligence & Data at Deloitte Consulting GmbH said, “By leveraging Document AI’s Custom Document Splitter, our client, Commerzbank, a large european bank, can effortlessly segment customer submissions tailored to their back-office requirements, significantly diminishing the need for extra manual sorting or routing. This integration paves the way towards seamless automation within the Document AI pipeline, delivering substantial business benefits.“

According to Kaïs Albichari – ML Tribe Tech Lead, G Cloud at IT services firm Devoteam, “Custom Document Splitter (CDS) has helped one of our clients in the financial services industry save significant time and improve data accuracy. By identifying which parts of documents they can discard and which they retain for entity extraction, CDS has helped the company automate its document processing tasks. The implementation resulted in a more efficient and streamlined workflow, freeing employees to focus on other tasks. Devoteam’s G Cloud team helped the company implement CDS and achieve these benefits.”

Frank Neugebauer, a Google Cloud Insurance Solutions Consultant, worked with a Fortune 100 insurance company and used CDS to create a model to split and classify millions of insurance documents with up to 98% accuracy. With this information, the insurer can better understand the nature of their unstructured data to inform business strategy, including volume for specific document types to inform extraction work. The customer considers this level of insight unprecedented in their 200+ year history.  

How to use Custom Document Splitter

You can leverage a simple interface in the Google Cloud Console and a set of public APIs to prepare training data, create and evaluate models, deploy a model into production, and call an API endpoint to split and classify document types. You can follow the documentation for instructions to create, train, evaluate, deploy, and run predictions with models.

Import and prepare training data

To get started, import and label documents to train and evaluate an ML model. 

To quickly build a training dataset, import single documents, one document per file, and bulk label them with the relevant document type. You can import one folder or multiple folders at once and choose the correct document type per folder. As shown in the next image, one import could have a folder with 200 bank statements, another folder with 200 W2s, another folder with 200 paystubs, etc., all of which are labeled at once while imported. Up to 30,000 documents and 100,000 pages can be inputted for training. This way, you can build a training dataset with hundreds of labeled documents per class in minutes. As always, if documents are already labeled using other tools, simply import labels with JSON in the Document format.

You can initiate training with a click of a button. Once you have trained a model, you can use it  to automatically label documents added to your dataset, letting you quickly build robust test and training datasets to evaluate and improve model performance.

To accurately evaluate a CDS model, import files which contain multiple document types within the same file and assign them to the test dataset. Then, use a simple interface to define document boundaries and types.

The ground truth you label in the test dataset is used to evaluate splitting and classification predictions from the CDS model.

Going into production

Once a model meets accuracy targets, it’s time to deploy into production and call the API endpoint to split and classify document types.

Getting started with Document AI Workbench 

Custom Document Splitter is publicly available in GA and ready to help customers automate document splitting and classification. Learn more via our Document AI Workbench web page, Document AI Workbench documentation or try it out in the Google Cloud Console.

One of the largest telecommunications companies in the world, Vodafone is at the forefront of building next-generation connectivity and a sustainable digital future.

Creating this digital future requires going beyond what’s possible today and unlocking significant investment in new technology and change. For Vodafone, a key driver is the use of artificial intelligence (AI) and machine learning (ML), enabling predictive capabilities in enhancing the customer experience, improving network performance, accelerating advances in research, and much more.

Following 18 months of hard work, Vodafone has made a huge leap forward in advancing its AI capabilities at scale with the launch of its “AI Booster” AI / ML platform. Led by the Global Big Data & AI organization under Vodafone Commercial, the platform will use the latest Google technology to enable the next generation of AI use cases, such as optimizing customer experiences, customer loyalty, and product recommendations.

Vodafone’s Commercial team has long focused on advancing its AI and ML capabilities to drive business results. Yet as demand grows, it is easier said than done to embed AI and ML into the fabric of the organization and rapidly build and deploy ML use cases at scale in a highly regulated industry. Accomplishing this task means not only having the right platform infrastructure, but also developing new skills, ways of working, and processes.

Having made meaningful strides in extracting value from data by moving it into a single source of truth on Google Cloud, Vodafone had already significantly increased efficiency, reduced data costs, and improved data quality. This enabled a plethora of use cases that generate business value using analytics and data science. The next step was building industrial scale ML capability, capable of handling thousands of ML models a day across 18+ countries, while streamlining data science processes and keeping up with technological growth.

Knowing they had to do something drastically different to scale successfully, along came the idea for AI Booster.

“To maximize business value at pace and scale, our vision was to enable fast creation and horizontal / vertical scaling of use cases in an automated, standardized manner. To do this, 18 months ago we set out to build a next-generation AI / ML platform based on new Google technology, some of which hadn’t even been announced yet.

“We knew it wouldn’t be easy. People said, ‘Shoot for the stars and you might get off the ground…’ Today, we’re really proud that AI Booster is truly taking off, and went live in almost double the markets we had originally planned. Together, we’ve used the best possible ML Ops tools and created Vodafone’s “AI Booster Platform” to make data scientists’ lives easier, maximise value and take co-creation and scaling of use cases globally to another level,” says Cornelia Schaurecker, Global Group Director for Big Data & AI at Vodafone.

AI Booster: a scalable, unified ML platform built entirely on Google Cloud

Google’s Vertex AI lets customers build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified platform. Built upon Vertex AI, Vodafone’s AI Booster is a fully managed cloud-native platform that integrates seamlessly with Vodafone’s Neuron platform, a data ocean built on Google Cloud.

“As a technology platform, we’re incredibly proud of building a cutting-edge MLOps platform based on best-in-class Google Cloud architecture with in-built automation, scalability and security. The result is we’re delivering more value from data science, while embedding reliability engineering principles throughout,” comments Ashish Vijayvargia, Analytics Product Lead at Vodafone

Indeed, while Vertex AI is at the core of the platform, it’s much more than that. With tools like Cloud Build and Artifact Registry for CI/CD, and Cloud Functions for automatically triggering Vertex Pipelines, automation is at the heart of driving efficiency and reducing operational overhead and deployment times. Today, users simply complete an online form, and then, within minutes, receive a fully functional AI Booster environment with all the right guardrails, controls, and approvals.

Not long ago it could take months to move a model from a proof of concept (PoC) to launching live in production. By focusing on ML operations (MLOps), the entire ML journey is now more cost-effective, faster, and flexible, all without compromising security. PoC-to-production can now be as little as four weeks, an 80% reduction.

Diving a bit deeper, Vodafone’s AI Booster Product Manager, Sebastian Mathalikunnel, summarizes key features of the platform: “Our overarching vision was a single ML platform-as-a-service that scales horizontally (business use cases across markets) and vertically (from PoC to Production). For this, we needed innovative solutions to make it both technically and commercially feasible. Selecting a few highlights, we:

• completely automated ML lifecycle compliance activities (drift / skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services;
• embedded security by design into the heart of the platform;
• capitalized on Google-native ML tooling using BQML, AutoML, Vertex AI and others;
• accelerated adoption through standardized and embedded ML templates.”

For the last point, Datatonic, a Google Cloud data and AI partner, was instrumental in building reusable MLOps Turbo Templates, a reference implementation of Vertex Pipelines, to accelerate building a production-ready MLOps solution on Google Cloud.

“Our team is devoted to solving complex challenges with data and AI, in a scalable way. From the start, we knew the extent of change Vodafone was embarking on with AI Booster. Through this open-source codebase, we’ve created a common standard for deploying ML models at scale on Google Cloud. The benefit to one data scientist alone is significant, so scaling this across hundreds of data scientists can really change the business,” says Jamie Curtis, Datatonic’s Practice Lead for MLOps.

Reimagining the data scientist & machine learning engineer experience

With the new technology platform in place, driving adoption across geographies and markets is the next challenge. The technology and process changes have a considerable impact on people’s roles, learning, and ways of working. For data scientists, non-core work now is supported by machines in the background—literally at the click of a button. They can spend time doing what they do best and discovering new tools to help them do the job.

With AI Booster, data scientists and ML engineers have already started to drive greater value and collaborate on innovative solutions. Supported by instructor-led and on-demand learning paths with Google Cloud, AI Booster is also shaping a culture of experimentation and learning.

Together We Can

Eighteen months in the making, AI Booster would not have happened without the dedication of teams across Vodafone, Datatonic, and Google Cloud. Googlers from across the globe were engaged in supporting Vodafone’s journey and continue to help build the next evolution of the platform.

Cornelia highlights that “all of this was only possible due to the incredible technology and teams at Vodafone and Google Cloud, who were flexible in listening to our requirements and even tweaking their products as a result. Alongside our ‘Spirit of Vodafone,’ which encourages experimenting and adapting fast, we’re able to optimize value for our customers and business. A huge thank you also to Datatonic, who were a critical partner throughout this journey and to Intel for their valuable funding contribution.”

The Google & Vodafone partnership continues to go from strength to strength, and together, we are accelerating the digital future and finding new ways to keep people connected.

“Vodafone’s flourishing relationship with Google Cloud is a vital aspect of our evolution toward becoming a world-leading tech communications company. It accelerates our ability to create faster, more scalable solutions to business challenges like improving customer loyalty and enhancing customer experience, whilst keeping Vodafone at the forefront of AI and data science,” says Cengiz Ucbenli, Global Head of Big Data and AI, Innovation, Governance at Vodafone.

Find out more about the work Google Cloud is doing to help Vodafone here, and to learn more about how Vertex AI capabilities continue to evolve, read about our recent Applied ML Summit.

As consumer expectations grow for more personalized, relevant, and assistive experiences, machine learning is becoming an invaluable tool to help meet those demands.

It’s helping marketers create smarter customer segmentations, deliver more relevant creative campaigns, and measure performance more effectively. In fact, 85% of executives believe AI will allow their companies to obtain or sustain a competitive advantage.¹

We created this guide to help you optimize your machine learning marketing efforts — whether you’re just starting out or you want to discover more benefits of machine learning.

Go to the Guide Now!