Case Study: Twitter is Taking Their CX to The Next Level with AutoML
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Editor’s note: Since launching its Spaces feature, Twitter has demonstrated that hearing people’s voices can bring conversations on Twitter to life in a completely new way. Next, it aimed to make it easier for customers to join and listen to live conversations they personally care about. In this blog, we learn how the Twitter Spaces Engineering team is bringing this vision to life with AutoML, powering a new ML heuristic which serves personalized recommendations to Twitter customers. The authors would like to thank Chuan Lu, Joe Balistreri, Chen-Rui Chou, Pablo Jablonski, Alberto Parrella, Pradip Thachile and Sam Lee from Twitter, as well as Helin Wang from Google, for contributions to this blog.
Since Twitter introduced Spaces in 2020 to enable live audio conversations on its platform, the Twitter Spaces Engineering team has been continually testing, building, and updating this feature in the open. Today, anyone can join, listen, and speak in a Space on Twitter, and the feature’s popularity has taken off. But this success also poses a challenge: with millions of people creating and joining Spaces at any time, how can they find the Spaces to engage with while they’re happening? Taking this as an opportunity to further improve the experience of its customers, Twitter has turned to machine learning (ML) and cloud technology for answers.
“ML fits into the natural progression of Twitter consumer and revenue product building, especially for a product feature such as Spaces,” explains Diem Nguyen, Senior Machine Learning Engineer and Data Scientist at Twitter. “We launched Spaces with a base-line algorithm using the ‘most popular’ heuristic which assumes that if a Space is popular, there’s a good chance you’d like it too. But our aim is to leverage ML to surface the most interesting and relevant Spaces to a particular Twitter customer, making it easier for them to find and join the conversations they personally care about. This is a complex functionality that Google Cloud ML capabilities help us to enable.”
Setting the stage for building new features with limited ML resources
While looking for the right tools to power this vision, Nguyen and her team started evaluating in December 2021 whether the Vertex AI platform and AutoML in particular could solve challenges observed when they first started building Spaces. These included a lack of dedicated ML resources to build and deploy the product feature, and the need to work on a multi-cloud environment.
“We had three key questions in mind during our assessment,” Nguyen explains. “Can we realistically deploy the AutoML model off-platform? Once deployed, can it solve for the request load that we get from the service we’re serving (in this case, the Spaces tab)? And finally, can we develop and maintain such a solution without a dedicated team of ML experts for this project?” The answer to all three questions was yes.
Positive answers motivated the Spaces Engineering team to take the solution to production in February 2022. “We started using AutoML Tables to train high-accuracy models with minimal ML expertise or effort, alleviating our resource constraint,” says Nguyen of the results. “Soon AutoML also stood out for its high performance and for supporting easy deployment beyond the Google Cloud Platform, making it ideal for this project hosted in a multi-cloud environment.”
Increasing customer engagement at speed with accurate ML predictions
With a classification model in place to predict the probability of user engagement in a particular Space, Twitter now aims to optimize its model with aggregated data around Twitter features that can help it better understand customer preferences. For example, if a customer has historically engaged with a particular topic and a new Space matches that topic, the ML model increases the score of that Space being served to that user on the Spaces tab.
Because Spaces are live audio conversations, the Spaces tab needs to be ranked to customers in near real time so they don’t miss out. With this in mind, Twitter’s model currently performs 900 queries per second on the Spaces tab, and evaluates 50,000 candidates per second. Meanwhile, 99% of these requests are faster than 100 milliseconds, and 90% of requests are faster than 50 milliseconds.
To measure the success of this project, Nguyen’s team conducted A/B experiments around key customer engagement metrics–A stands for the ‘most popular’ heuristic previously in production, and B is the new AutoML model which seeks to personalize Spaces recommendations to the interests of individual Twitter users. Three months into the project, the numbers were encouraging. “After deploying our AutoML Tables solution we saw an increase of 1.96% in Spaces daily active customers, which is one of our key metrics. We also noticed an increase of 1.99% in Spaces join in rates, and an increase of 8.42% in user clicks to explore a Space,” Nguyen shares. “These are positive signals that users are now engaging more with the Spaces tab service on the Twitter app, which is exactly what we set out to do with this project.”
Powering new use cases with hands-off ML frameworks
With this first solution running in production to improve the performance of the Spaces tab, Nguyen starts to ask how else it might support the experience of Twitter users moving forward. “The Spaces tab is a small surface on the Twitter app. With our current ML solution we’re some distance away from serving our home tab traffic, which is where a lot of our traffic happens and therefore would involve a much bigger-scale operation. Getting there will take some work but we’re evaluating the possibility of optimizing our model performance for this in collaboration with Google Cloud,” says Nguyen.
“As a product-led company, we focus on continually improving the customer experience and we want to iterate faster to get to that point. AutoML brings that value to our product teams because it is so hands-off. You don’t need to write any model code in order to reap the benefits from this machine learning framework; AutoML automatically experiments with many different model architectures and comes up with a state-of-the-art model that addresses your needs. So while it is not a one-size-fits-all solution, it is a great solution with the potential to power many more Twitter use cases,” she concludes.
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Building Ethical AI: Why Organizations Need to Define Their Own Principles
In this video, learn about the importance of responsible AI, and how Google implements responsible AI in their products. You will also get an introduction to Google’s 7 AI principles.
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Medical Data Breakthrough: Google Aids PicnicHealth’s Growth
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In the fragmented world of U.S. healthcare, patients often have to wait in line or on hold, navigate multiple patient portals, and fill out numerous request forms—all in pursuit of their own medical history. Healthcare technology startup PicnicHealth is on a mission to put control back with the patient, where it belongs.
PicnicHealth’s growth, from closing successful venture rounds to winning machine learning (ML) competitions, speaks to not only improvements and opportunities in healthcare, but also how startups are leveraging Google Workspace and Google Cloud services to accelerate momentum.
The company does the heavy lifting of collecting records and leverages human-in-the-loop ML to transcribe and validate them with an abstraction team of medical professionals. The records are then structured into a complete medical history that patients can access and share with providers to get better care.
But PicnicHealth helps to improve patient health on more than one front. It allows patients to contribute their data to de-identified medical research, building high-quality, anonymized datasets that researchers and life sciences companies can use to better understand disease progression and treatment in the real world.
Google Workspace has been part of PicnicHealth from day one, helping the founders collaborate and shape the company’s vision using cloud-synced documents and spreadsheets to collaborate and model predictions. “I’ve had a Gmail account since 2006, and in college 100% of people worked out of Google Docs. Workspace continues to be the best choice for online collaboration, and that’s why it’s still the default standard for startups,” said Troy Astorino, CoFounder & CTO of PicnicHealth. “When we started PicnicHealth, my co-founder Noga Leviner was in San Francisco and I was in Southern California, and of course we used Workspace.”
Today, Google Workspace continues to play a central role in the company’s collaboration. “We create design documents in Google Docs, primarily for engineering and product changes, and get really healthy, vibrant discussions through comments,” noted Astorino. “This practice has grown beyond engineering and is used for everything from how the company operates to communication norms.”
Google Workspace offers everything the team needs to collaborate, no matter where employees are. PicnicHealth’s team has spread from San Francisco to being distributed across the country and around the world. Instant collaboration is crucial.
“We work in a complex domain where people need a lot of information to make good decisions,” said Astorino. “Google Workspace allows us to operate in a mode of default transparency, where people can easily get the information they need even if it wasn’t intentionally or directly shared with them. Whether it’s working in Docs or scheduling in Calendar, we can operate much more effectively than we could otherwise.”
By any measure, PicnicHealth’s trajectory is one of record success. The startup is a 2014 alumni of Y Combinator, a program that helped launch household names like Airbnb, DoorDash, and Dropbox. Three years later, the team went on to win the $1 million grand prize at Google’s Machine Learning Competition. And the momentum has continued— PicnicHealth has recently announced a $60 million Series C round, bringing the amount raised to date to over $100 million. With the Series C, PicnicHealth is investing in expanding its reach to more patients across over 30 diseases.
As a healthcare startup, PicnicHealth faced a very particular set of challenges, especially when working with and accessing data. Data fragmentation and interoperability are only some of the challenges of realizing the value of big data in the cloud. The healthcare industry is notoriously difficult to navigate due to sensitive data protection laws and regulations like the Health Insurance Portability and Accountability Act (HIPAA).
PicnicHealth started in the cloud on Amazon Web Services (AWS). However, after migrating over to Kubernetes and facing an expanding list of requirements for HIPAA compliance, the company started to explore alternatives.
“We needed to be HIPAA compliant, which was going to be painful on AWS, and we wanted to get away from managing and operating our own Kubernetes clusters,” recalled Astorino. “We had heard good things about GKE (Google Kubernetes Engine). And particularly valuable for us, — many technical requirements you need for HIPAA compliance are configured by default on Google Cloud.”
PicnicHealth would have had to implement a lot of changes and get specialized instance types to get their existing configuration to work. So, they began experimenting with Google Cloud and discovered a much smoother experience.
“It was a lot easier to manage in terms of product setup and developer experience,” said Astorino. “There is a sane product hierarchy of resources you can access and use through Google Cloud and the relationships between them, from coordinated IAM (identity and access management) to using Google Groups for granting permissions. Overall, it’s cleaner.”
Astorino added that the move has also opened the doors to taking advantage of other services in the Google Cloud ecosystem like Cloud SQL, BigQuery, and Cloud Composer. PicnicHealth also uses Security Command Center because it easily integrates with everything but also helps meet various compliance frameworks’ requirements, providing visibility, near-real-time asset discovery, and security information and event management.
But most importantly, the integrated ecosystem has simplified the work needed for PicnicHealth to create a secure environment for employees to use when working with sensitive medical records while still providing all the tools they need. For example, abstractors not only use Google Workspace but also have Chromebooks because they are easy to manage and secure.
Altogether, Google Cloud helps form a technology stack that has enabled the startup to build a massive labeled dataset containing over 100 million labeled medical data concepts. In turn, it accelerates PicnicHealth’s ability to generate highly-performant AI models and feed other ML pipelines, which has been vital for processing and reviewing data at scale.
To learn more about how Google Workspace and Google Cloud help startups like PicnicHealth accelerate their journey, visit our startups solutions pages for Google Workspace and Google Cloud.
Google Cloud’s No-Cost Skill Badge: Up Your Generative AI Game
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Generative AI is a rapidly expanding technology with a wide range of potential applications. Google Cloud Learning is thrilled to offer a new, no-cost Generative AI Fundamentals skill badge. This skill badge is designed for anyone eager to learn about the power of generative AI. No technical skills or prior knowledge required!
For those new to digital credentials, Google Cloud skill badges are digital credentials issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Individuals can earn skill badges on Google Cloud Skills Boost, and can share their skill badge to their social media profile and resume.
Watch the short videos in the generative AI courses and complete the final quiz to earn the Generative AI Fundamentals skill badge pictured below. In as little as 120 minutes, you will learn the basics of how generative AI works, how Google Cloud AI technology can be used by businesses and individuals, and how the principles of responsible AI lead to ethical decisions about the use of generative AI.
By earning the skill badge, you will demonstrate your understanding of foundational concepts in generative AI.
The topics covered in the courses include:
1. Introduction to Generative AI
- Explain how generative AI works
- Describe generative AI model types
- Describe generative AI applications
2. Introduction to Large Language Models
- Define large language models (LLMs)
- Describe LLM use cases
- Explain prompt tuning
- Describe Google’s generative AI development tools
3. Introduction to Responsible AI
- Identify the need for a responsible AI practice within an organization
- Recognize that decisions made at all stages of a project make an impact in Responsible AI
- Recognize that organizations can design an AI infrastructure to fit their own business needs and values
Earn the skill badge and show off your generative AI knowledge today! And for more content to help you stay up to date with generative AI, check out “The Prompt” and our generative AI primer for executives on Transform with Google Cloud.
Telegraph Media Group Creates More Marketing Opportunities With Google Cloud Platform
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London-based Telegraph Media Group is a multimedia news publisher with titles including The Daily Telegraph, The Sunday Telegraph, The Telegraph website and The Telegraph digital edition. The Daily Telegraph is the UK’s best-selling quality daily newspaper with a long-established history of over 160 years and is unique in having maintained its broadsheet format.
In order to support its advertising, subscriptions and ecommerce businesses, and better align advertisers with possible customers, Telegraph Media Group leverages an enterprise data warehouse to gain insights about its existing and potential subscriber base.
Analysis performed by the system helps the organization implement strategies designed to increase user engagement, boost ad impressions, drive conversions, determine optimal ad creative and support accurate planning and forecasting.
Traditionally, Telegraph Media Group had leveraged legacy technologies to run its enterprise data warehouse which was expensive and delayed timely access to actionable insights.
In an effort to modernize its platform, the organization tested a system running on Hadoop using an alternative cloud services provider, but found the solution cost prohibitive and challenging from a technology and operational perspective when producing results in real time.
Find out how using Google Cloud Platform benefitted the Telegraph Media Group.
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Responsible AI: From Theory to Practice
In less than 10 years, AI will be the number one driver of global GDP growth. And organizations that achieve AI absorption will be the leaders of the global economy.
But as fast as AI is progressing, it also requires more care and attention from a responsibility standpoint. A more accurate and powerful vision AI technology, for example, when used in a harmful way can lead to harmful and intentional misuse, unintentional failure modes, and loss of personal privacy contributing to severe, real-life consequences for individuals.
Listen to Tracy Frey, Director, Product Strategy & Operations, Cloud AI – Google Cloud discuss implementing AI Principles into well-known products. Hear approaches in place for applying AI Principles in the product development process, including user research, product design, product reviews, testing, documentation, and marketing.
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