It’s simply amazing to witness how some of our systems integrators employ Google Cloud solutions to drive innovation in ways we at Google may never have considered—especially in healthcare.  According to analyst firm MarketsandMarkets, the market for the Cloud in healthcare is projected to grow 43% between 2020 and 2025 to nearly $65 billion, fueled by the need for better technology infrastructures and faster digital transformation.  

Healthcare and life sciences communities are looking to Google Cloud and its Service engagement model partners to improve collaboration and activate the power of medical data.  These transformations deliver robust data analytics and bring a much deeper perspective into health epidemics like COVID-19 to help save lives.

In the healthcare and life sciences industry, our Google Cloud partners and customers provide constant energy and inspiration–and are the magic in some key healthcare innovations globally.  

Let me show you how they are solving real-world business challenges.

Improving collaboration in Healthcare

Cloudbakers and Comanche County Memorial Hospital transitioned 2,000 employees to Google Workspace to improve collaboration, reduce costs, and increase security. By implementing a system that requires less maintenance while enabling mobile access to data and true collaboration, Comanche County Memorial Hospital saves $175,000 annually on licenses and helps medical professionals spend more time with patients.

“We chose Google Workspace because it cost a quarter of what we were paying previously, offers the kind of modern features that healthcare facilities need, and gave us data security and peace of mind.” —James Wellman, CIO, Comanche County Memorial Hospital

Accessing previously locked down medical data

Google Cloud and Quantiphi supported advances in cloud-based machine learning services to reduce infrastructure costs, unlock new paths of treatment, and dramatically reduce the amount of time it takes to evaluate scanned imagery following a stroke.  John Hopkins University BIOS Division has been working on medical imaging to accelerate insights from scans on approximately 500 patients from 2,500 hours to 90 minutes, and lead to more accurate decision-making for brain injury patients that will ultimately improve medical outcomes.  

“We’ve aligned closely with the goal of showing that a cloud-based, AI-driven approach is robust and that it can be performed while protecting personal PHI. In terms of costs, the cloud has definitely reduced some of the traditional financial demands of our research.” —Daniel F. Hanley, Jr., M.D., Director, Johns Hopkins University BIOS Division

Improving data access with the flexibility of Google Cloud

With the help of MediaAgility, TRIARQ migrated to Google Cloud to modernize their platform, build new applications, and expand their global footprint.  With BigQuery, TRIARQ can now consolidate all transactional data, past and present, into a single location to report on specific insights and predict future data from e-prescriptions to complex revenue-cycle data and value-base analysis.

“The future of this industry will need to be a global one, and we can no longer be stuck in legacy systems if we want to survive. Having a team that is passionate about supporting us in this journey is definitely a plus point.”—Yaw Kwakye, Co-founder and Chief Architect, TRIARQ

Increasing visibility to COVID-19 outbreaks for actionable insights

As part of its response to the COVID-19 pandemic, HCA Healthcare chose to work with Google Cloud and SADA to create a national portal that increases visibility into outbreaks in 3,100 counties across the country in just 8 weeks.  Now, the portal generates 30,000 new analytical views each day that can help inform private and public sector decision making for reopenings, closures, hot spots, and many other population health management activities. 

“This project required deep knowledge of AI, and consumer-facing platforms, as well as healthcare. Google brought together the ideal combination of product, people, and partners. Google Cloud’s healthcare-specific products along with SADA’s expertise in the healthcare IT space made this partnership the perfect choice to move quickly and intelligently.”—Dr. Edmund Jackson, Chief Data Officer, HCA Healthcare

We’re committed to building the technology, resources, and services through Partner Advantage to help our partners address this opportunity.  Looking for a solution focused partner in your region who has achieved Expertise and/or Specialization in your industry?  Search our Global Partner Directory.  Not yet a Google Cloud partner? Visit Partner Advantage and learn how to become one today!

One of the biggest challenges when serving machine learning models is delivering predictions in near real-time. Whether you’re a retailer generating recommendations for users shopping on your site, or a food service company estimating delivery time, being able to serve results with low latency is crucial. That’s why we’re excited to announce Private Endpoints on Vertex AI, a new feature in Vertex Predictions. Through VPC Peering, you can set up a private connection to talk to your endpoint without your data ever traversing the public internet, resulting in increased security and lower latency for online predictions. 

Configuring VPC Network Peering

Before you make use of a Private Endpoint, you’ll first need to create connections between your VPC (Virtual Private Cloud) network and Vertex AI.  A VPC network is a global resource that consists of regional virtual subnetworks, known as subnets, in data centers, all connected by a global network. You can think of a VPC network the same way you’d think of a physical network, except that it’s virtualized within GCP. If you’re new to cloud networking and would like to learn more, check out this introductory video on VPCs.

With VPC Network Peering, you can connect internal IP addresses across two VPC networks, regardless of whether they belong to the same project or the same organization. As a result, all traffic stays within Google’s network.

Deploying Models with Vertex Predictions

Vertex Predictions is a serverless way to serve machine learning models. You can host your model in the cloud and make predictions through a REST API. If your use case requires online predictions, you’ll need to deploy your model to an endpoint. Deploying a model to an endpoint associates physical resources with the model so it can serve predictions with low latency. 

When deploying a model to an endpoint, you can specify details such as the machine type, and parameters for autoscaling. Additionally, you now have the option to create a Private Endpoint. Because your data never traverses the public internet, Private Endpoints offer security benefits in addition to reducing the time your system takes to serve the prediction when it receives the request. The overhead introduced by Private Endpoints is minimal, achieving performance nearly identical to DIY serving on GKE or GCE.  There is also no payload size limit for models deployed on the private endpoint.

Creating a Private Endpoint on Vertex AI is simple.

In the Models section of the Cloud console, select the model resource you want to deploy.

Next, select DEPLOY TO ENDPOINT

In the window on the right hand side of the console, navigate to the Access section and select Private. You’ll need to add the full name of the VPC network for which your deployment should be peered.

Note that many other managed services on GCP support VPC peering, such as Vertex Training, Cloud SQL, and Firestore. Endpoints is the latest to join that list.

What’s Next?

Now you know the basics of VPC Peering and how to use Private Endpoints on Vertex AI. If you want to learn more about configuring VPCs, check out this overview guide. And if you’re interested to learn more about how to use Vertex AI to support your ML workflow, check out this introductory video. Now it’s time for you to deploy your own ML model to a Private Endpoint for super speedy predictions!

In 2013, long before the world was discussing clean energy and sustainable practices, two IIT Madras graduates — Swapnil Jain and Tarun Mehta — had an idea to develop India’s first-ever electrical scooter.

This was at a time when auto manufacturers were still focusing on fossil-fuel-driven vehicles and ‘eco-friendly’ mobility solutions were more a trendy alternative catering to a niche market.

The duo founded Ather Energy in 2013 and launched their first fully-electric scooter, the Ather S340, in Bengaluru in 2016. Since then, the company has released several new models into the market and is planning to expand to eight more cities by the end of the year.

To support the smooth running of their vehicles, lower costs, improve time to market, and create great customer experience, Ather turned to Google Cloud.

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