The COVID-19 pandemic continues to push the healthcare and life sciences industry in entirely new ways. In record time, we’ve witnessed public health officials, vaccine developers, equipment manufacturers, and essential workers take life saving actions—regularly putting their own lives at risk—to respond to the exceptional challenges of our time. 

Yet after all the turmoil and uncertainty of the pandemic, record breaking levels of investment continue to come into the market to fuel innovations. 

Vaccine development is now measured in weeks rather than years; providers are leveraging telehealth technologies to improve the physician and patient experience; and individuals have embraced a variety of devices to assume greater ownership and control of their personal health. 

With that in mind, here are five of the many innovations I see driving healthcare and life sciences for at least the next 12 months: 

1. Unleashing the power of healthcare and life sciences data

People have arguably never had as much access and understanding to their personal health data than they can in 2022. They have the ability to understand their genetic makeup, medical history, family history, and activity levels to ensure they are living a healthy lifestyle. Taken together, this longitudinal profile can become the basis for more personalized medicine. Add wearable devices to the mix and patients gain real- or near-real-time updates on their health status. 

Further, global regulatory agencies have continued to mandate the need for providers to maintain a longitudinal health record that provides a holistic view of the patient across all the encounters they have had with a health system. Physician surveys conducted by the The Harris Poll and Google Cloud show that a 360-degree view of the patient, across all provider encounters, leads to faster, more accurate diagnosis, and better outcomes. 

If secure data access and interoperability can begin to include insurers, researchers, public health officials, and others in the field, the powerful network effects benefiting patients will only grow. When combined with those longitudinal phenome profiles, the opportunities for personalized and preventative medicine enter a whole new era.

2. Healthcare and life sciences M&A boom continues

Although the pandemic initially slowed activity on mergers and acquisitions in the early part of 2020, the healthcare and life sciences industry has seen a rapid rebound and acceleration of deals ever since. The success of COVID-19 vaccines, the importance of telehealth, and the focus on molecular modeling and genomic-based drug development have all boosted investment as organizations look to enter new markets, develop new therapies, and leverage low interest rates while they last. 

In 2021, the total funding of US digital health startups surpassed $29 billion across 729 deals, according to advisory Rock Health. That’s almost double the levels of 2020, which itself set records. Analysts at PWC meanwhile estimate M&A investments in biopharmaceutical and life sciences could approach $400 billion this year across all sub-sectors

Clearly, the pandemic has been a primary driver of investment as the focus on healthcare has dramatically increased. Yet the increased activity also reflects changing business models and emerging technologies that are now required to compete in the rapidly evolving space. For organizations to capitalize on these investments, it will take not only great vision and intellectual property but also the right technologies—like cloud—and the right data interoperability models, to make partnerships and acquisitions more scalable, feasible, and seamless.

3. Transforming the patient experience at a new rate

The pandemic has shined a spotlight on the inefficiencies and complexities that exist in healthcare markets across the globe. As wave after wave has surged, global healthcare systems remain overwhelmed on most every aspect of patients’ treatment journeys. Even before COVID-19, healthcare was already one of the largest spend areas for governments around the world. The pandemic has only exacerbated the known issues. 

Clearly, administrators, regulators, physicians, nurses, and patients would agree that the processes and models need to change. There’s a need to maintain this momentum and even increase the tempo to achieve lasting change.

Take telehealth. Within months of the start of the pandemic, providers moved to provide more remote capabilities so physicians could still meet with patients virtually to ensure health and safety on all sides. Payers recognized the importance of telehealth and began to update reimbursement rules. And organizations are now reimagining policies in areas such as prior authorization, submission, and adjudication to reduce complexity and bureaucracy while improving responsiveness.

Looking beyond the system, organizations are also recognizing and deepening their understanding of the structural and social determinants of health that impact patient care and health outcomes, especially for historically underserved communities.  Private and public sectors are learning from, and increasingly partnering with, the social sciences, public health, biomedical informatics, computer science, public policy and community groups around how to build a mI’ore equitable and inclusive consumer products and Health IT strategies. 

The newfound levels of transparency, visibility, and accountability that patients, caregivers, and organizations are achieving will ultimately increase competition and provide a more effective, equitable, efficient and, above all, healthier marketplace for all patients. As we move past the worst of the pandemic, regulators and organizations should keep fighting for progress over business as usual.

4. AI is now a core competency for Drug Development

The ability of organizations like Pfizer, Moderna, Johnson & Johnson, and Astrazeneca to develop COVID-19 vaccines has been a remarkable accomplishment—particularly the historic speed with which they were created and deployed. This innovation acceleration was largely enabled by the use of new drug development platforms that allow researchers to use artificial intelligence and machine learning to model protein and cellular interactions to rapidly advance the science.

No longer must researchers rely on traditional laboratory testing (and retesting). With their improved understanding of the molecular and genetic structure of a patient and, for example, their tumor, researchers can use AI to enable simulations on computers rather than testing in live conditions. This technology can process thousands, even millions of simulations to help  identify high-potential candidates for treatment consideration and subsequent analysis. 

AI-enabled drug discovery models can eliminate months and years from the research process, which can reduce the time to develop a drug and accelerate the time to treatment for an individual patient. As just one example, consider the work on AlphaFold2 by Google’s DeepMind unit who leverages AI to predict effective protein shapes for new drugs. Healthcare and life sciences organizations already recognize the potential of AI. Now comes the investments to leverage this rapidly evolving technology to support their efforts now and in the future.   

5. Ecosystem partnerships tackle complexity and spur innovation

As the importance and growth of the healthcare and life sciences industry continues, we will see even more new players and partnerships emerging to address old problems in new ways. This trend will touch all aspects of the healthcare value chain and will, increasingly, see three- and four-player partnerships emerge to address the complex challenges of today’s healthcare marketplace.

Technology will continue to play a key role as capabilities and platforms will transform all aspects of the marketplace. Cell phones, wearable devices, and other technologies will provide real-time updates and notifications to patients on everything from glucose levels to payments for healthcare services. Voice recognition software will document physician and patient discussions to reduce the burden of record keeping. Real-world data will be used to simplify and confidentially recruit patients for participation in clinical trials. 

New players will continue to enter the market to improve health outcomes and reduce costs. Major retailers are among the companies extending their pharmacies to provide additional diagnostic and concierge services, saving patients from additional appointments while boosting prevention. Community organizations are emerging to help identify and care for underserved communities whose health outcomes are significantly lower than the average patient. 

For all the exhausting and heart-wrenching challenges of the past two years, the opportunities the pandemic has laid bare cannot be overlooked. We owe it to those who have worked and fought so hard for every life to forge even more new partnerships—and make it easier to do so—so that the next crisis, when it does arise, will never be as bad as the one we’re now conquering. Technology can be the enabler in this effort and help bring us together to continue to conquer the challenges that lie ahead.

Financial institutions across the globe rely on real-time indices to inform real-time portfolio valuations, to provide benchmarks for other investments, and as a basis for passive investment instruments including exchange-traded products (ETPs). This reliance is growing—the index industry dramatically expanded in 2020, reaching revenues of $4.08 billion.

Today, indices are calculated and distributed by index providers with proximity and access to underlying asset data, and with differentiating real-time data processing capabilities. These providers offer subscriptions to real-time feeds of index prices and publish the constituents, calculation methodology, and update frequency for each index.

But as new assets, markets, and data sources have proliferated, financial institutions have developed new requirements. Financial institutions will need to quickly create bespoke and frequently updating indices that represent a specific actual or theoretical portfolio, with its unique constituents and weightings.

In other words, existing index providers and other financial institutions alike will need mechanisms for rapid creation of real-time indices. This blog post’s focus—an index publication pipeline collaboratively developed by CME Group and Google Cloud—is an example of such a mechanism. 

The pipeline closely approximates a particular CME Group index benchmark, but with far greater frequency (in near real time vs. daily) than its official counterpart. It does so by leveraging open-source models such as Apache Beam and cloud-based technologies such as Dataflow, which automatically scales pipelines based on inbound data volume.

Machine learning’s production problem

In the past decade, advances in AI toolchains have enabled faster ML model training—and yet a majority of ML models are still not making it into production. As organizations endeavor to develop their ML capabilities, they soon realize that a real-world ML system is comprised of a small amount of ML code embedded in a network of complex and large ancillary components. Each component brings its own development and operational challenges, which are met by bringing a DevOps methodology to the ML system, commonly referred to as MLOps (Machine Learning Operations). To apply ML to business problems, a firm must develop continuous delivery and automation pipelines for ML.

This index publication collaboration is instructive because it demonstrates MLOps best practices for just such a pipeline. One Apache Beam pipeline, suited for operating on both batch and streaming data, extracts insights and packages them for downstream consumers. These consumers may include ML pipelines that, thanks to Apache Beam, require only one code path for inference across batch and real-time data sources. The pipeline is run inside Google Cloud’s Dataflow execution engine, greatly simplifying management of underlying compute resources. 

But the collaboration’s value is not constrained to the ML and data science realm. The project shows that consumers of the Apache Beam pipeline’s insights may also include traditional business intelligence dashboards and reporting tools. It also demonstrates the simplicity and economy of cloud-based time series data such as CME Smart Stream, which is metered by the hour, quickly and automatically provisioned, and consumable at a per-product-code (not per-feed) level.

A focus on real-time processing for financial services

To illustrate the above points, the collaboration applies data engineering and MLOps best practices to a financial services problem. We chose the financial services domain because many financial institutions do not yet have real-time market data processing or MLOps capabilities today, owing to a significant gap on either side of their ML/AI objectives.

Upstream from ML/AI models, financial institutions often experience a data engineering gap. For many financial institutions, batch processes have sufficiently addressed business requirements. As a result, the temporal nature of the time series data underlying these processes is deemphasized. For example, the original purpose of most trade booking systems was to capture a trade and ensure that it found its way to the middle and back office for settlement. It was not built with ML/AI in mind, and its underlying data therefore has not been packaged for consumption by ML/AI processes. 

And downstream from ML/AI models, financial institutions often encounter the aforementioned “ML production problem.”

As ML/AI becomes ever more strategic, these two gaps have left many financial institutions in a conundrum—unable to train ML models for lack of properly packaged time series data, and unmotivated to package time series data for lack of ML models. By recreating a key energy market index using open-source libraries and cloud-based tools, this collaboration demonstrates that for the financial services domain a solution to this conundrum is more accessible today than ever. 

Creating a new index

We modeled our new index after one of CME Group’s many index benchmarks. The particular index expresses the value of a basket of three New York Mercantile Exchange—listed energy futures as a single price. Today, CME Group publishes the index at the end of the day by calculating the settlement price of each underlying futures contract, and then weighing and summing these values. 

While CME Group does not currently publish this index in real time, this collaboration aims to create a near real-time solution leveraging Google Cloud capabilities and CME Group market data delivered via CME Smart Stream. However, in order to publish the value so frequently—every five seconds, with 40-second publish latency—this collaboration’s pipeline has to solve a number of challenges in near-real time.

First, the pipeline must process sparse data from three separate trades feeds in memory to create open-high-low-close (OHLC) bars. More specifically, for five-second windows for each of the three front-month (and sometimes second-month) energy contracts, a bar must be produced. This is solved by using the Apache Beam library to implement functions which, when executed on Dataflow, automatically scale out as input load increases. The bars must be time-aligned across the underlying feeds, which is greatly simplified by Beam’s watermark feature. And for intervals in which no tick data is observed, the Beam library is used to pull forward the last value received, yielding perfect gap-free bars for downstream processors.

Second, the pipeline must calculate volume-weighted average price (VWAP) in near real-time for each front-month contract. The VWAP calculations are also written using the Beam API and executed on Dataflow. Each of these functions requires visibility of each element in the time window, so the functions cannot be arbitrarily scaled out. Nonetheless, this is tractable because their input—OHLC bars—is manageably small.

Third, the pipeline must replicate CME Group’s specific settlement price methodology for each contract. The rules specify whether to use VWAP or another source as price, depending on certain conditions. They also specify how to weigh combinations of monthly contracts during a roll period. The pipeline again encapsulates these requirements as an Apache Beam class, and joins the separate price streams at the correct time boundary.

The end result is a new stream publishing bespoke index data to a Google Cloud Pub/Sub topic thousands of times daily, enabling AI models as well as traditional industry index usage, dashboards, and other tools to assist real-time decision making. The stream’s pipeline uses open source libraries that solve common time series problems out-of-the box, and cloud-based services to reduce the user’s operational and scaling burden.

The importance of cloud-based data

The promise of cloud-based pipeline execution services cannot be realized using legacy data access patterns, which often require market data users to colocate and configure servers and network gear. Such patterns inject expense and scaling complexity into the pipeline’s overall operation, diverting resources from the adoption of MLOps best practices. Instead, a newer, cloud-based access pattern—in which resources subscribe to data streams inexpensively, rapidly and programatically—is necessary.

In 2018, CME Group identified the customer need for accessible futures and options market data. CME Group collaborated with Google Cloud to launch CME Smart Stream, which distributes CME Group’s real-time market data across Google Cloud’s global infrastructure with sub-second latency. Any customer with a CME Group data usage license and a Google Cloud project can consume this data for an hourly usage fee, without purchasing and configuring servers and network gear.

CME Smart Stream met this index pipeline’s requirements for cost-effective, cloud-based streaming data, but this is just one use case. Since the launch of a CME Smart Stream offering on Google Cloud, globally dispersed firms have adopted the solution. For example, Coin Metrics has been using the offering to better inform its customers in the crypto markets. According to CME Group, Smart Stream has become popular with new customers as the fastest, simplest way to access CME Group’s market data from anywhere in the world. 

Adapt the design pattern to your needs

By combining cloud-based data, open-source libraries, and cloud-based pipeline execution services, we created a real-time index using the same constituents as its end-of-day counterpart. Additionally, financial institutions will find this approach addresses many other challenges—real-time valuation of a large set of portfolios; benchmark creation for new ETPs; or external publication of new indices.

Give it a try

This approach is available to help you meet your organization’s needs. Please review our user guide, whose Tutorials section provides a step-by-step guide to constructing a simple Apache Beam pipeline to generate metrics on streaming data in real-time, and connecting a new data source to the pipeline. We’ll be discussing this topic in CME Group’s webinar End-to-End Market Data Solutions in the Cloud at 10:30 am ET on June 16th.

Organizations today build data lakes to process, manage and store large amounts of data that  originate from different sources both on-premise and on cloud. As part of their data lake strategy, organizations want to leverage some of the leading OSS frameworks such as Apache Spark for data processing, Presto as a query engine and Open Formats for storing data such as Delta Lake for the flexibility to run anywhere and avoiding lock-ins.

Traditionally, some of the major challenges with building and deploying such an architecture were:

• Object Storage was not well suited for handling mutating data and engineering teams spent a lot of time in building workarounds for this
• Google Cloud provided the benefit of running Spark, Presto and other varieties of clusters with the Dataproc service, but one of the challenges with such deployments was the lack of a central Hive Metastore service which allowed for sharing of metadata across multiple clusters.
• Lack of integration and interoperability across different Open Source projects

To solve for these problems, Google Cloud and the Open Source community now offers:

• Native Delta Lake support in Dataproc, a managed OSS Big Data stack for building a data lake with Google Cloud Storage, an object storage that can handle mutations
• A managed Hive Metastore service called Dataproc Metastore which is natively integrated with Dataproc for common metadata management and discovery across different types of Dataproc clusters
• Spark 3.0 and Delta 0.7.0 now allows for registering Delta tables with the Hive Metastore which allows for a common metastore repository that can be accessed by different clusters.

Architecture

Here’s what a standard Open Cloud Datalake deployment on GCP might consist of:

1. Apache Spark running on Dataproc with native Delta Lake Support
2. Google Cloud Storage as the central data lake repository which stores data in Delta format
3. Dataproc Metastore service acting as the central catalog that can be integrated with different Dataproc clusters
4. Presto running on Dataproc for interactive queries

Such an integration provides several benefits:

1. Managed Hive Metastore service
2. Integration with Data Catalog for data governance
3. Multiple ephemeral clusters with shared metadata
4. Out of the box integration with open file formats and standards

Reference implementation

Below is a step by step guide for a reference implementation of setting up the infrastructure and running a sample application

Setup

The first thing we would need to do is set up 4 things:

1. Google Cloud Storage bucket for storing our data
2. Dataproc Metastore Service
3. Delta Cluster to run a Spark Application that stores data in Delta format
4. Presto Cluster which will be leveraged for interactive queries

Create a Google Cloud Storage bucket

Create a Google Cloud Storage bucket with the following command using a unique name.

  gsutil mb gs://<your-bucket-name>

Create a Dataproc Metastore service

Create a Dataproc Metastore service with the name “demo-service” and with version 3.1.2. Choose a region such as us-central1. Set this and your project id as environment variables.

  REGION=<your-region>
PROJECT_ID=<your-project-id>
gcloud metastore services create demo-service \
    --hive-metastore-version=3.1.2 \
    --location=${REGION}

Create a Dataproc cluster with Delta Lake

Create a Dataproc cluster which is connected to the Dataproc Metastore service created in the previous step and is in the same region. This cluster will be used to populate the data lake. The jars needed to use Delta Lake are available by default on Dataproc image version 1.5+

  gcloud dataproc clusters create delta-cluster \
    --dataproc-metastore=projects/${PROJECT_ID}/locations/us-central1/services/demo-service \
    --region=${REGION} \
    --image-version=2.0.0-RC22-debian10

Create a  Dataproc cluster with Presto 

Create a Dataproc cluster in us-central1 region with the Presto Optional Component and connected to the Dataproc Metastore service.

  gcloud dataproc clusters create presto-cluster \
    --dataproc-metastore=projects/${PROJECT_ID}/locations/us-central1/services/demo-service \
    --region=${REGION} \
    --image-version=2.0-debian10 \
    --optional-components=PRESTO \
    --enable-component-gateway

Spark Application

Once the clusters are created we can log into the Spark Shell by SSHing into the master node of our Dataproc cluster “delta-cluster”.. Once logged into the master node the next step is to start the Spark Shell with the delta jar files which are already available in the Dataproc cluster. The below command needs to be executed to start the Spark Shell. Then, generate some data.

  spark-shell --jars /usr/lib/delta/jars/delta-core.jar
import io.delta.tables._
import org.apache.spark.sql.functions._
// Simulate application data
val orig_df = Seq(
  (1L, 3.0), (2L, -1.0), (3L, 0.0)
).toDF("x", "y")

# Write Initial Delta format to GCS

Write the data to GCS with the following command, replacing the project ID.

  orig_df.write.mode("append").format("delta").save("gs://<your-bucket-name>/first-delta-table")

# Ensure that data is read properly from Spark

Confirm the data is written to GCS with the following command, replacing the project ID.

  spark.read.format("delta").load("gs://<your-bucket-name>/first-delta-table").show()

Once the data has been written we need to generate the manifest files so that Presto can read the data once the table is created via the metastore service.

# Generate manifest files

  val deltaTable = DeltaTable.forPath("gs://<your-bucket-name>/first-delta-table")
deltaTable.generate("symlink_format_manifest")

With Spark 3.0 and Delta 0.7.0 we now have the ability to create a Delta table in Hive metastore. To create the table below command can be used. More details can be found here 

# Create Table in Hive metastore

  spark.sql("CREATE TABLE my_first_table (x bigint,y double) ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.SymlinkTextInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat' LOCATION 'gs://<your-bucket-name>/first-delta-table/_symlink_format_manifest'")

Once the table is created in Spark, log into the Presto cluster in a new window and verify the data. The steps to log into the Presto cluster and start the Presto shell can be found here.

#Verify Data in Presto

  presto:default> select * from hive.default.my_first_table;
 x  |  y  
----+------
  2 | -1.0
  3 |  0.0
  1 |  3.0

Once we verify that the data can be read via Presto the next step is to look at schema evolution. To test this feature out we create a new dataframe with an extra column called “z” as shown below:

# Schema Evolution in Spark

Switch back to your Delta cluster’s Spark shell and enable the automatic schema evolution flag

  spark.sql("SET spark.databricks.delta.schema.autoMerge.enabled = true")

Once this flag has been enabled  create a new dataframe that has a new set of rows to be inserted along with a new column 

  val merge_df = Seq(
  (10L, 30.0, "a"), (20L, -10.0, "b"), (30L, 100.0, "c")
).toDF("x", "y", "z")

Once the dataframe has been created we leverage the Delta Merge function to UPDATE existing data and INSERT new data 

# Use Delta Merge Statement to handle automatic schema evolution and add new rows

  deltaTable.alias("o").merge(merge_df.as("n"),"o.x = n.x").whenMatched.updateAll().whenNotMatched.insertAll().execute()

As a next step we would need to do two things for the data to reflect in Presto:

1. Generate updated schema manifest files so that Presto is aware of the updated data
2. Modify the table schema so that Presto is aware of the new column.

When the data in a Delta table is updated you must regenerate the manifests using either of the following approaches:

• Update explicitly: After all the data updates, you can run the generate operation to update the manifests.
• Update automatically: You can configure a Delta table so that all write operations on the table automatically update the manifests. To enable this automatic mode, you can set the corresponding table property using the following SQL command.

  ALTER TABLE delta.<path-to-delta-table> SET TBLPROPERTIES(delta.compatibility.symlinkFormatManifest.enabled=true)

However, in this particular case we will use the explicit method to generate the manifest files again

  deltaTable.generate("symlink_format_manifest")

Once the manifest file has been re-created the next step is to update the schema in Hive metastore for Presto to be aware of the new column. This can be done in multiple ways, one of the ways to do this is shown below:

# Promote Schema Changes via Delta to Presto

  val schema_evolution = "ALTER TABLE my_first_table ADD COLUMN ( " + merge_df.schema.toDDL.replace(orig_df.schema.toDDL,"").substring(1) + ")"
spark.sql(s"$schema_evolution")

Once these changes are done we can now verify the new data and new column in Presto as shown below:

# Verify changes in Presto

  presto:default> select * hive.default.from my_first_table;
 x  |   y   |  z  
----+-------+------
 20 | -10.0 | b    
  2 |  -1.0 | NULL
  3 |   0.0 | NULL
 30 | 100.0 | c    
 10 |  30.0 | a    
  1 |   3.0 | NULL

In summary, this article demonstrated:

1. Set up the Hive metastore service using Dataproc Metastore, spin up Spark with Delta lake and Presto clusters using Dataproc
2. Integrate the Hive metastore service with the different Dataproc clusters
3. Build an end to end application that can run on an OSS Datalake platform powered by different GCP services

Next steps

If you are interested in building an Open Data Platform on GCP please look at the Dataproc Metastore service for which the details are available here and for details around the Dataproc service please refer to the documentation available here. In addition, refer to this blog which explains in detail the different open storage formats such as Delta & Iceberg that are natively supported within the Dataproc service.

Japan is home to millions of vending machines installed on streets and in buildings, sports stadiums and other facilities. Vending machine owners and operators, including beverage manufacturers, stock these machines with different product combinations depending on location and demand. For example, they primarily display coffee and energy drinks in machines placed in offices and sports drinks and mineral water in machines at sports facilities. The combinations also vary by season: for example, owners and operators may display cold beverages in summer and hot beverages in winter. 

Traditionally, vending machine operators have relied on the intuition and experience of sales managers to determine the optimum product mix for each vending machine. However, in recent years, manufacturers such as Coca-Cola Bottlers Japan (CCBJ) have turned to data to analyze and make strategic decisions about when and where to locate products in machines.

CCBJ is the number one Coca-Cola bottler in Asia and vending machines comprise the bulk of its business. The organization operates about 700,000 machines across Tokyo, Osaka, Kyoto, and 35 prefectures. Minori Matsuda, Google Developer Expert and also Data Science Manager at CCBJ, says “The billions of data records collected from 700,000 physical devices are a great asset and a treasure trove we can take advantage of.”

Minori points out that when considering the mix of products in vending machines in sporting facilities, the managers naturally assume sports drinks would generally sell well. However, analysis of purchase data – including hot drinks and hot drinks plus sports drinks – found many parents purchased sweet drinks such as milk tea when they attended games or sessions involving their children.  “Analyzing data gives us new discoveries and, by using catchy storytelling techniques from exploratory data analysis, we are instilling a data culture within our company,” he says. “It’s worth creating by looking at facts rather than making assumptions!”

Minori believes that to analyze the vast amount of data collected from more than 700,000 vending machines, the business needs a powerful analytical platform. However, until recently, CCBJ had to extract data for analysis from its core systems, load this data into a warehouse it created and perform the required analyses.  The billions of records of data generated across the fleet – including transaction data – exposed some challenges for traditional analysis platforms. They could not efficiently process data at a considerable scale: it could take a day to return results and required extensive maintenance due to the size.

CCBJ considered building a machine learning (ML) platform as a layer on top of existing systems in August 2020 and opted for Google Cloud the following month.  “I feel that Google Cloud has an edge in all products and is very well thought out,“ says Minori, noting the scalability and cost of the platform allow the business to take a ‘trial and error’ approach to achieve the best outcomes from ML. Google Cloud also delivered the required visibility and flexibility to help the business deliver change every day against key performance indicators. 

MLOps platform streamlines ML pipeline development

CCBJ built its analysis platform using Vertex AI (formerly AI Platform) centered on a BigQuery analytics data warehouse, and partly using AutoML for tabular data. “We have created a prediction model of where to place vending machines, what products are lined up in the machines and at what price, how much they will sell, and implemented a mechanism that can be analyzed on a map,” says Minori, adding that building the platform with Google Cloud was not difficult. “We were able to realize it in a short period of time with a sense of speed, from platform examination to introduction, prediction model training, on-site proof of concept to rollout.”

The new data analytics platform of CCBJ consists of the following parts:

Data Sources

• The data collected from the vending machines are all stored on BigQuery.

Data Discovery and Feature Engineering

• Minori and other data scientists at CCBJ are using Vertex Notebooks, where they access the data on BigQuery by executing SQL queries directly from the Notebooks. This environment is used for the data discovery process and feature engineering. 

ML Training

• For ML training, CCBJ uses AutoML for Tabular data, Custom model training on Vertex AI, and BigQuery ML. AutoML gives model performance with AUC curves and also feature importance graphs.

ML Prediction and Serving

• For ML prediction, CCBJ uses Online Prediction for AutoML models and Online Prediction for custom models for real-time prediction when the salesperson finds the interesting point
• Batch Prediction is used for generating a large prediction map that covers the whole country
• The prediction results are distributed to sales managers’ tablets

CCBJ started constructing the platform in September 2020, and completed it within a month. The business has conducted proofs of concept at its base in Kyoto since February 2021, and since April, has rolled out the platform to sales managers in 35 prefectures in one metropolitan area. “Data analysis is built into the day-to-day routines of sales managers with 100% utilization,” says Minori. “They can utilize the prediction results on tablets that were able to achieve pretty high accuracy from the start.”

The hardest part was the education of sales managers in the field; having them understand the reasoning behind the ML prediction results for particular outcomes, so they could be convinced to make use of the results. “For example, regarding a new installation location predicted by the model, it seemed that there was no effective information for installation from the map information, but when I actually went there, there was a motorcycle shop and it was a place where young people who like motorcycles gathered,” says Minori. “Or there is a small meeting place where the elderly in the neighborhood are active. 

“In many cases, new discoveries that cannot be understood from map information alone can be derived from the data.”

Minori also points to a phenomenon whereby humans pursued and confirmed factors inferred by the model – meaning that once they experienced analysis and it worked effectively, they asked why the same type of analysis or prediction could not be undertaken next time. The resulting cycle of more inquiries generated, more information gathered and more data captured for analysis meant the accuracy of results was improved.

Minori describes Vertex AI as having a number of strengths in helping CCBJ build a ML data analysis platform. “One of the major merits of Vertex AI was that we were able to realize MLOps that streamlines the entire development life cycle from construction of the ML pipeline to its execution,” he says.

With near real-time data analysis through Google Cloud, CCBJ teams can spend time developing strategies rather than waiting for data requested from the IT systems department. Exploratory data analysis is also considerably easier as repeated trial and error has greatly improved the accuracy of analyses. Before we used Machine Learning, most machine placement processes were done by human senses, by looking at a map to find the suggestion points. By using Machine Learning to generate a massive number of placement point suggestions, the efficiency of routing of salespeople has been dramatically improved. 

In the future, CCBJ aims to automate the continuous training pipeline with Vertex AI. “CCBJ is a tech company that operates in the food industry,” says Minori. With the organization operating a vending machine network of 700,000 units, it would like to create new businesses based on utilization and analyzing data. Some of these businesses may be based on Sustainable Development Goals (SDGs) initiatives such as the utilization of recycled PET bottles, measures to prevent food loss and ways of using vending machines to contribute to local communities, which we have been working on for some time. It would be interesting if we could collaborate with Google Cloud on these in the future.”

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.”