There’s a lot we can learn from combining technology with science to help support the development of amazing discoveries. By using an AI system to predict protein shapes, we have the potential to accelerate research in every field of biology. Inside every cell in your body, billions of tiny molecular machines are hard at work. They are what allow your eyes to detect light, your neurons to fire, and the ‘instructions’ in your DNA to be read.

These intricate machines are known as proteins.

The protein folding puzzle

Protein folding is something that occurs naturally so that proteins become biologically functional, but it’s a complex process that sometimes fails. For decades, scientists have been trying to find a method to reliably predict a protein’s structure from its sequence of amino acids so we can better understand how proteins work.

The challenge? There are over 200 million known distinct proteins. Each one has a unique 3D shape that determines how it works and what it does. Because there are so many sequences and determining their 3-D structure experimentally is so time-consuming and expensive, scientists only know the exact structure of a tiny fraction of the proteins. And these experimental methods still fall far short of reliable statistical accuracy.

Deepmind’s gigantic leap

In 2020, Alphabet’s artificial intelligence research arm, DeepMind, made a massive breakthrough in predicting protein structures using a deep learning model called AlphaFold.

AlphaFold is trained on publicly available data consisting of about 170,000 protein structures, and is the first computational method that can regularly predict the 3D shape of a protein, at scale with a high degree of accuracy.

AlphaFold has already sent waves throughout the scientific community and has demonstrated the potential for AI to aid fundamental scientific discovery. Recently, Deepmind has made AlphaFold predictions available and open source to anyone. To date, more than 500,000 researchers from 190 countries have accessed the AlphaFold protein structure database to get closer to finding life-saving cures for diseases like Leishmaniasis and Chagas.

And now Deepmind has expanded the set of available predictions by more than 200 times (from nearly 1 million to nearly 214 million) to cover almost all cataloged proteins found in nature.

Open source predictions available on Google Cloud

Together, Google Cloud and Deepmind have released this dataset of predicted protein structures for plants, bacteria, animals, and other organisms as part of the Google Cloud Public Dataset program to enable bulk downloads at no cost. That means you can also create custom queries of the dataset using BigQuery!

Running AlphaFold on Google Cloud Vertex AI

Let’s say you want to run AlphaFold on your own in order to get protein structure predictions against your own set of data. There are a few challenges to keep in mind:

• You need to set up feature engineering against genetic sequence databases
• Preprocess data
• And run those inputs against pre-trained models

All of this requires allocating CPUs or GPUs, hosting a notebook environment, and scaling up for larger experiments. It’s hard to build and configure an on-premise system or cloud server to use AlphaFold whether you just want to try it out or run it at scale as a large organization.

That’s why we’re excited to share a deep integration between Google Cloud and Deepmind. On top of the Public Datasets program we have created end-to-end code samples for AlphaFold on Vertex AI, a managed end-to-end ML platform, to help address these challenges and speed up deployment. With AlphaFold on Vertex AI, you can manage a data science or machine learning workflow in a single development environment. You get access to pre-configured compute, storage, and end-to-end production notebooks. We have removed the heavy lifting needed to set up new ML environments, automate orchestration, and manage large clusters.

The AlphaFold inference workflow can be simplified with Vertex AI: from data preparation to feature engineering and deployment. Unlike the manual set up, the orchestrator makes it possible to parallelize steps, get predictions faster, and with better tracking.

Try it out first using Vertex AI Workbench

For those of you who want to try out a simplified version of AlphaFold, we have a Colab notebook that uses no templates (homologous structures) and a selected portion of the BFD database. You can deploy right on Vertex AI Workbench, which lets you specify a custom container image that we’ve already created for you. You’ll be able to:

1. Configure access to genetic databases
2. Configure GPU acceleration
3. Search against genetic databases
4. Use the pre-processed results as inputs to the AlphaFold model locally

In a little over an hour you can harness the power of AlphaFold to generate 3-D protein structures from amino acid sequences.

Run hundreds of experiments reliably using Vertex AI Pipelines

For organizations that want to run a full blown version of AlphaFold for many protein folding experiments a week, you’ll want an ML pipeline orchestrator. The AlphaFold Batch Inference solution is a set of code samples that uses Vertex AI Pipelines to support hundreds of concurrent inference pipelines with higher throughput to help you run experiments at scale. The solution uses Vertex AI Pipelines as an orchestrator and runtime, Vertex ML Metadata for metadata and artifacts, and Cloud Filestore to manage databases.

Because it’s built on Vertex AI Pipelines, you can automate, monitor, and experiment with interdependent parts of an ML workflow. The minimized inference elapsed times mean what normally would take you days, can now take you hours.

The solution includes two example pipelines:

1. The universal pipeline solution mirrors the exact logic in DeepMind’s open source inference script but decoupled into discrete tasks so you can run the same experiments faster, more efficiently, and with better tracking.

2. The customized pipeline solution shows you how to further optimize the inference workflow by parallelizing feature engineering steps so you can plug in your own database sources.

You get example components, pipelines, and notebooks to start, analyze, and recompile pipelines on different GPUs.

The AlphaFold Vertex AI Workbench solution is great for experimental use, while the AlphaFold Batch Inference solution on Vertex AI Pipelines is great for doing protein folding at scale with a strong process for reproducibility and tracking.

Now go forth and save the world!

Okay maybe that’s a bit hyperbolic, but this is inspiring stuff! What started as a 50 year challenge, to the discovery of AlphaFold, to being able to run it on Google Cloud, researchers, developers, and science enthusiasts now have access to one of the most pivotal advancements in the medical world. Even a non-specialist can easily use a Vertex AI notebook to exercise a simplified version of AlphaFold. The next answers to the mysteries of life and discovery of disease treatments have never felt more attainable. With these no-cost solutions to run AlphaFold on Vertex AI and the Public Dataset, you can help propel us in this worldwide endeavor.

Learn more about healthcare and life sciences solutions on Google Cloud here.

If you have feedback or want to share your experience with me, reach out to me at @stephr_wong.

Patients about to undergo a healthcare procedure understandably want the best medical professionals they can get. But how can they know which doctors have had the most experience and the best outcomes with that particular procedure? How can they make an informed decision about which doctor to select when the information they have is limited to the doctor’s practice area and subjective reviews from other patients?

MD Insider is working to solve that problem using machine learning (ML) to objectively analyze doctor performance. By analyzing data from thousands of institutions and millions of doctor-patient interactions and medical events, MD Insider identifies physician performance insights based on their experience and outcomes. Insights are then integrated into a triage engine, that enables consumers to search for and schedule appointments with providers who meet their clinical criteria and convenience preferences, such as insurances accepted, office hours, locations, and language.

MD Insider also offers robust data APIs to help health systems, health plans, and employers reduce costs and improve quality of care. Payers use the APIs to curate high-quality provider networks and manage provider directories. Examples of MD Insider’s data APIs include Provider Experience and Share of Practice metrics, Provider Quality and Outcomes, Network Modeling, Expert Clinical Search Taxonomy, Find a Provider, Acute-Care Hospital Quality, and Provider and Facility Metadata.

“We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

MD Insider is continuously ingesting the latest performance data about physicians and analyzing billions of rows of data. Requiring constant scalability, the company was born in the cloud; however, it had difficulty configuring server instances for the optimal balance of memory and CPU, and its Hadoop cluster had to be kept running 24/7. Network performance was often slow for no apparent reason. As a result, failure rates from node timeouts increased, and costs grew along with the data. MD Insider had to estimate its usage and pay up front, and received little financial benefit from sustained use commitments.

Knowing that data would continue to grow, MD Insider decided to move its data services — the most demanding and complex portion of its infrastructure — to Google Cloud Platform (GCP), and took advantage of GCP managed services for container management and big data analytics.

“One of the reasons we decided to move to Google Cloud Platform is because it feels like a unified, well-designed cloud architecture and pricing model,” says Ed Holsinger, Lead Data Engineer and Head of Data Science at MD Insider. “We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

“Moving to Google Cloud Platform and using Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost. Our data scientists have more power than ever before to generate insights for our customers.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

5x the performance, 75% less cost

MD Insider now uses Kubernetes Engine to automate container management and deploy clusters in minutes with just a few clicks. When hundreds of machines are required to analyze a large dataset, automation in Kubernetes Engine deploys ML models as containers, each of which manages the full lifecycle of its task, including scaling up resources, deploying results, and scaling back down when the task is finished. It’s easy for MD Insider to specify exactly how much CPU and memory each container needs, helping maximize performance while reducing costs.

“Moving to Google Cloud Platform and Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost,” says Ed. “Our data scientists have more power than ever before to generate insights for our customers. Data scoring jobs that used to take three business days now take four hours.”

Adds Galen Meurer, Senior Software Engineer at MD Insider: “Even if all Google Cloud Platform had to offer was Kubernetes Engine, I would still want to use it. Previously we spent up to 30% of our time managing our container infrastructure, which we can now use for product development.”

A foundation for data science

MD Insider was happy to find that GCP offers a wide variety of managed services. For example, the company is supplementing its Kubernetes Engine clusters with BigQuery for its big data masters and selection jobs, enabling scientists to analyze new and different types of data as well as analyze larger datasets in less time. MD Insider also uses Cloud Storage for big data staging and Cloud Dataproc to run managed Apache Spark clusters for data processing.

“Google Cloud Platform gives us an incredibly powerful cloud architecture for data engineering and data science,” says Eric Wilson, CEO of MD Insider. “That gives our scientists independence, they can do what they need to do without waiting and with no contention between them.”

A developer-friendly platform

Migrating its data services was such a success that MD Insider decided to move the rest of its infrastructure to GCP, including the front end for its web application. Since the migration, MD Insider has experienced no unplanned downtime on GCP, allowing it to easily meet the 99.5% uptime SLA it promises to customers. It’s also taking advantage of Build Triggers in Kubernetes Engine to automate container builds and reduce build times by more than 40%. Production code can be updated in seconds, with no impact to end users other than making new features available.

“GCP has simplified our workflow in so many ways, from intelligent load balancing to content delivery and automating builds,” says Matthew Frey, Software Engineer. “Everything on GCP is cohesive and developer friendly, with a superior UI and better network performance than other cloud providers.”

Ryan Beaini, Senior Software Engineer at MD Insider, agrees: “Since we moved to GCP, our developers are definitely happier. The pain and the headaches we experienced because of the limitations of our previous toolset all went away.”

“We’re a small company, but what we’re doing is incredibly important. We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

—Eric Wilson, CEO, MD Insider

Securing billions of rows of clinical and non-clinical healthcare data

As a healthcare technology company, MD Insider processes billions of rows of clinical and non-clinical healthcare data. To control user access to GCP, it uses Identity & Access Management (IAM) along with Yubico YubiKeys for hardware-based two-factor authentication when logging into Google Workspace. MD Insider takes comfort that GCP encrypts data at rest by default, and encrypts and authenticates data in transit when data moves outside physical boundaries not controlled by Google or on behalf of Google.

“On GCP, everything that we need to be encrypted for compliance purposes is encrypted, which is fantastic,” says Eric. “When I tell our potential clients and partners about the resources that Google has dedicated to security, it gives them the confidence that their data will be protected.”

Transforming how teams work

As a growing company, MD Insider must collaborate seamlessly between offices in California, Colorado, and Illinois. It relies on Google Workspace for communication and productivity, using Docs, Sheets, and Slides to drive the business. Employee and team files are stored in Drive, and meetings are conducted via Google Meet with Chromebox for Meetings videoconferencing hardware kits. Google Workspace also helps MD Insider maintain information security by authenticating email domains with digital signatures in Gmail and scanning outgoing email using Gmail Data Loss Prevention (DLP).

“I use Google Workspace every day, and everyone else here does too,” says Eric. “Team Drives are a big time saver for us. We’ve let our previous office software expire, because there’s no need to pay for those licenses anymore.”

Promoting healthcare transparency

With GCP helping MD Insider increase velocity and momentum, the company is making exciting progress. For example, it has entered into a strategic partnership with Zelis Healthcare, which will use MD Insider’s API to provide insights for a next-gen analytics platform that will give health plans unprecedented transparency around physician performance.

“We’re a small company, but what we’re doing is incredibly important,” says Eric. “We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

*Google Workspace was formerly known as G Suite prior to Oct. 6, 2020.

As the volume of data that people and businesses produce continues to grow exponentially, it goes without saying that data-driven approaches are critical for tech companies and startups across all industries. But our conversations with customers, as well as numerous industry commentaries, reiterate that managing data and extracting value from it remains difficult, especially with scale.

Numerous factors underpin the challenges, including access to and storage of data, inconsistent tools, new and evolving data sources and formats, compliance concerns, and security considerations. To help you identify and solve these challenges, we’ve created a new whitepaper, “The future of data will be unified, flexible, and accessible,” which explores many of the most common reasons our customers tell us they’re choosing Google Cloud to get the most out of their data.

For example, you might need to combine data in legacy systems with new technologies. Does this mean moving all your data to the cloud? Should it be in one cloud or distributed across several? How do you extract real value from all of this data without creating more silos?

You might also be limited to analyzing your data in batch instead of processing it in real-time, adding complexity to your architecture and necessitating expensive maintenance to combat latency. Or you might be struggling with unstructured data, with no scalable way to analyze and manage it. Again, the factors are numerous—but many of them accrue to inadequate access to data, often exacerbated by silos, and insufficient ability to process and understand it.

The modern tech stack should be a streaming stack that scales with your data, provides real-time analytics, incorporates and understands different types of data, and lets you use AI/ML to predictively derive insights and operationalize processes. These requirements mean that to effectively leverage your data assets:

• Data should be unified across your entire company, even across suppliers, partners, and platforms., eliminating organizational and technology silos.
• Unstructured data should be unlocked and leveraged in your analytics strategy.
• The technology stack should be unified and flexible enough to support use cases ranging from analysis of offline data to real-time streaming and application of ML without maintaining multiple bespoke tech stacks.
• The technology stack should be accessible on-demand, with support for different platforms, programming languages, tools, and open standards compatible with your employees’ existing skill sets.

With these requirements met, you’ll be equipped to maximize your data, whether that means discerning and adapting to changing customer expectations or understanding and optimizing how your data engineers and data scientists spend their time. In coming weeks, we’ll explore aspects of the whitepaper in additional blog posts—but if you’re ready to dive in now, and to steer your tech company or startup towards success by making your data better work for you, click here to download your copy, free of charge.

2021 has been a high-stakes year for call centers, with many organizations forced to rapidly scale up their call center operations in response to ongoing pandemic disruptions. We’re proud that 2021 has also been an amazing year for Google Cloud’s Contact Center AI (CCAI), which has helped our customers adapt and thrive, despite the challenging conditions. 

Beginning in January, we launched Dialogflow CX in GA. Agent Assist preview was released in May. Most recently, CCAI Insights GA was announced at Google Cloud NEXT in October. During NEXT, we shared lots of great content on how you can use CCAI to improve your customer experience with these breakout sessions:

• Using CCAI Insights to Better Understand Your Customers 
• Customer Impact with Conversational AI 
• Drive Results by Transforming the Customer Experience with AI-Powered Business Messages

But don’t just take our word for it. We also got a chance to hear how some companies are using CCAI to better reach their own customers, including The Home Depot, TELUS, and Love Holidays. We partnered with CDW to discuss  transforming the contact center with AI and with Quantiphi on how to migrate from Dialogflow EX to CX. Our integration with Looker Block also makes CCAI Insights even more powerful by visualizing contact center metrics. Over the summer, we hosted a Dialogflow CX competition with more than 1,100 participants. Just last month, we showed how we’ve enabled businesses to use AI in their interactions using Google Business Messages. Looking to the future, we talked about the future in our article,“Reimagining your Customer Experience with Conversational AI.”  

Amwell,  a U.S.-based telehealth company that is launching CCAI, including the recently launched CCAI Insights, is among the enterprises harnessing AI to transform its call centers. “With Contact Center AI, we aim to digitize our support for improved operational efficiency and elevated analytics capabilities, while enhancing the customer experience for patients, providers, and staff,” says Paul Johnson, SVP Client Services at Amwell. “Contact Center AI Insights will allow Amwell to better understand why our platform users are reaching out to support and how they feel about the overall experience – valuable insights for our support organization.”

As we recap the momentum of CCAI for 2021, it’s also a good time to review exactly how CCAI works.

What is CCAI?

As the volume of customer calls increases, it’s becoming even more important to make the most of human agents’ time to lower costs and improve customer experiences. CCAI enables you to do just that: it frees human agents to concentrate on more complex calls by providing them with real-time information to better handle those calls. 

Single source of intelligence: Contact Center AI provides a consistent, high-quality conversational experience across all channels and platforms, both human and virtual. Because the “brains” of CCAI are centralized in the cloud, you can apply consistent intelligence across every application in the customer journey. 

Ability to go off-script: Huge cost savings can be realized by having a virtual agent handle voice calls. This is easier said than done, however,  because conversations rarely are completely linear; instead, they meander from topic to topic, which is difficult to handle programmatically in a fixed-path Interactive Voice Response (IVR) system.   

Contact Center AI has the ability to go “off script” — to let callers go down tangents or side paths to the main conversation, while still tracking towards the main objective of the call.  With CCAI, your virtual agents can answer complex questions and complete complicated tasks, including allowing for unexpected stops and starts, unusual word choices, or implied meanings.  Developers can define supplemental questions, and CCAI can easily retain the context, answer the supplemental question, and come back to the main flow.

Versatile fulfillment: CCAI has the ability to handle multiple use cases for the customer with the same virtual agent, which enables you to fully automate routine tasks and deflect calls.  The same virtual agent can take a payment, update information like a phone number, give a customer information on their balance, and process information for other tasks, all within the same conversational flow.

How does CCAI work?

CCAI has three key components:

• Conversation Core: This is the central AI brain that underpins CCAI and its ability to understand, talk, and interact.  It enables and orchestrates high-quality conversational experiences at scale making it possible for customers to have  conversations with a virtual agent that are as good as conversations with a human agent.
  • Understand – Speech-to-text speech recognition understands what customers are saying regardless of how they phrase things, what vocabulary they use, what accent they have, and so on. 
  • Talk – Text-to-speech enables virtual agents to respond to customers in a natural, human-like manner that pushes the conversation along, rather than frustrate them.
  • Interact – Dialogflow identifies customer intent and determines the appropriate next step. You can build conversational flows in a point-and-click interface, and generate automated ML models for human-like conversational experiences.
• Virtual agents with Dialogflow: This component automates interactions with customers, using natural conversation to identify and address their issues. Virtual agents enable customers to get immediate help anytime, day or night. Agent Assist: This component brings AI to human agents to increase the quality of their work, while decreasing their average handling time.  Agent Assist shares initial context and provides real-time, turn-by-turn guidance to coach agents through business processes, as well as full call transcriptions that agents can edit and file quickly. 
• CCAI Insights: CCAI Insights aids your contact center management team in making better data driven decisions for their business by breaking down conversations using natural language processing and machine learning. Having this information allows your business to reduce manual analysis and focus on decision making like which conversations need your attention, where to deploy virtual agent automation to have the biggest impact and how to address your customer needs.  

How does CCAI create experiences for agents and customers? 

When a user initiates a chat or voice call and the contact center provider connects them with CCAI, a virtual agent engages with the user, understands their intent, and fulfills the request by connecting to the backend. If necessary, the call can be handed off to a human agent, who sees the transcript of the interaction with the virtual agent, gets feedback from the knowledge base to respond to queries in real time, and receives a summary of the call at the end. Insights help you understand what happened during the virtual agent and live agent sessions. The result is improved customer experiences and CSAT scores, lower agent handling times, and more time for human agents to spend on more complicated customer issues.

And there you have it: a quick overview of CCAI and its progress in 2021. For more details, check out the documentation or our CCAI solutions page.https://www.youtube.com/embed/6_Gilug2QYw?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.