Editor’s note: The post is part of a series highlighting our awesome partners, and their solutions, that are Built with BigQuery

In the field of producing engaging video content such as ads, many marketers ignore the power of data to improve their creative efforts to meet the consumers’ need for personalized messages. The demand for creative tech to efficiently personalize is real as marketers need personalized video Ads to reach their audience with the right message at the right time. Data, Insights and Technology are the main ingredients to deliver this value while ensuring security and privacy requirements are met. The Connected-Stories team partnered with Google Cloud to build a platform for Ad personalization. Google Data Cloud and BigQuery are at the forefront to assimilate data, leverage ML models, create personalized ads, and capitalize on real-time intelligence as the core features of the Connected-Stories NEXT platform.

Connected-Stories NEXT is an end-to-end creative management platform to develop, serve, and optimize interactive video and display ads that scale across any channel. The platform ingests first-party data to create custom ML models, measure numerous third-party data points to help brands develop unique customer journeys and create videos that their data signals can drive. An intelligent feedback loop passes real-time data back, enabling brands to make data-driven and actionable video ads that take the brand’s campaigns to the next level.

The core use case of the NEXT platform revolves around collecting user’s interaction data and optimizing for precision and speed to create an actionable Ad experience that is personalized for each user. The platform processes complex data points to create interactive data visualizations that allow for accurate analysis. The platform uses Vertex AI to access managed tools, workflows, and infrastructure to build, deploy, and scale ML models that have improved the accuracy to identify segments for further analysis.

The platform ingests 200M data events with peaks and valleys of activity. These events are processed to generate dashboards that enable users to visualize metrics based on filters in real-time. These dashboards have high performance requirements in terms of a responsive user interface under constantly changing data dimensions.

Google Cloud’s serverless stack coupled with limitless data cloud infrastructure has been the core to the NEXT platform’s data-driven innovation. The growing volume of data ingested, streamed and processed were scaled uniformly across the compute, storage and analytical layers of solution. A lean development team at Connected-Stories were able to focus all-in on the solution, while the serverless stack scaled, lowered attack service in terms of security and optimized the cost footprint through pay-as-you-go features.

BigQuery has been the backbone to support the vast amounts of data spreading over multiple geos resulting in workloads running at petabyte scale. BigQuery’s fully managed serverless architecture, real-time streaming, built-in machine learning and rich business intelligence capabilities distinguishes itself from a cloud data warehouse. It is the foundation needed to approach data and serve users in an unlimited number of ways. For an application with zero tolerance for failure, given its fully managed nature, BigQuery handles replication, recovery, data distributed optimization and management.

The platform’s requirements include the need for low maintenance, constantly ingesting and refreshing data and smart-tuning of aggregated data. These capabilities can be implemented by BigQuery’s materialized views feature. Materialized views are useful for precomputed views that regularly cache query results for better performance. These views possess the innate feature to read only the delta change from base tables and calculate the up-to-date aggregations. Materialized views impart faster outputs and consume fewer resources while reducing the cost footprint.

Some key considerations in using Google cloud and focusing on the Serverless stack include: quick onboarding to development, prototyping in short sprints and ease of preparing data in a rapidly changing environment. Typical considerations around low code / no code include data transformation, aggregation and reduced deployment time. These considerations are fulfilled through using serverless capabilities within Google Cloud such as PubSub, Cloud Storage, Cloud Run, Cloud Composer, Dataflow and BigQuery as described in the Architecture diagram below. The use of each of these components and services are described below.

1. Input/Ingest: At a high-level, microservices hosted in Cloud Run collect and aggregate incoming Ads events.
2. Enrichment: The output of this stage is a Pub-Sub message enriched with more attributes based on a pre-configured campaign.
3. Store: a Cloud Dataflow streaming job to create text files in Cloud Storage buckets.
4. Trigger: Cloud Composer triggers the spark jobs based on text files to process and group them to produce desired output as one record per impression, a logical group of events.
5. Deploy: Cloud Build is then used to automate all deployments.

Thus far, all Google cloud managed services work together to ingest, store and trigger the orchestration, all of which are scalable based on configurations including autoscaling capabilities.

1. Visualization: A visualization tool reads data from BigQuery to compute pre-aggregations required for each dashboard.
2. Data Model Evolution considerations: Though the solution served the purpose of creating pre-aggregations, as the data model evolved by adding a column or creating a new table, it led to recreating pre-aggregations and querying the data again. Alternatively, creating aggregate tables as an extra output of current ETLs seemed like a viable option. However, this would increase the cost and complexity of jobs. A similar situation to reprocess or update aggregated tables would occur as data is updated.

Precomputed views of data that is periodically cached are critical to reach the audience with the right message at the right time.

1. Performance: In order to increase the performance of the platform, we need to have regularly precomputed views of the data, cached .
2. Materialized Views: Consumers of these views needed faster response times, to consume fewer resources and output only the changes in comparison to a base table. BigQuery Materialized views were used to solve this very requirement. Materialized views have been highly leveraged to optimize the design resulting in lesser maintenance and access to fresh data with high performance with a relatively low technical investment in creating and maintaining SQL code.
3. Dashboards: Application dashboards pointing to the Materialized views are highly performant and provide a view into fresh data.
4. Custom Reports with Vertex AI Notebooks: Vertex AI notebooks directly read data from BigQuery to produce custom reports for a subset of customers. Vertex AI has been hugely beneficial to data analysts, where an environment with pre-installed libraries simplifies the readiness to use. Vertex AI Workbench notebooks are used to share these reports within the team allowing them to work always on the cloud without having the need to download data at any time. Besides, it increases the velocity to develop and test ML models faster.

The NEXT platform has yielded benefits such as customers having the ability to create unique consumer journeys powered by AI / ML personalization triggers, using first-party data and business intelligence tools to capitalize on real-time creative intelligence, which is a dashboard to measure campaign performance for cross-functional teams to analyze the impact of Ad content experience at a granular level. All of these while ensuring controlled access to data to enrich data without moving across clouds. The NEXT platform can keep up with increased demands for agility, scalability and reliability through the underlying usage of Google Cloud.

Partnering with Google, in the context of the Google Built with BigQuery program has surfaced the differentiated value in areas of creating interactive personalized Ads by using real-time data. In addition, by sharing this data across organizations as assets, ML models have fueled higher levels of innovation. Connected-Stories plan to deepen the penetration into the entire spectrum of services offered in the AI/ML area to enhance core functionality and provide newer capabilities to the platform.

Click here to learn more about Connected-Stories NEXT Platform capabilities.

The Built with BigQuery Advantage for ISVs

Through Built with BigQuery, launched in April ‘22 as part of Google Data Cloud Summit, Google is helping tech companies like Connected-Stories co-innovate in building applications that leverage Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs. Participating companies can:

• Get started fast with a Google-funded, pre-configured sandbox.
• Accelerate product design and architecture through access to designated technical experts from the ISV Center of Excellence who can share insights from key use cases, architectural patterns, and best practices encountered in the field.
• Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

The Google Data Cloud spectrum of products and specifically BigQuery give ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge and expanding partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in and exercise choice.

We thank the Google Cloud and Connected-Stories team members who co-authored the blog: Connected-Stories: Luna Catini, Marketing Director, Google: Sujit Khasnis, Cloud Partner Engineering

Let’s face it: in the globalized world, which is now more than ever a digital demand world, you need to scale and reach your customers right where they’re at. Translation is a critical piece of that, whether you’re translating a website in multiple languages or releasing a document, a piece of software, or training materials.

Manual translation does not scale, which is why machine translation, powered by machine learning (ML), is becoming more important to our customers.  Machine translation has historically been challenging because of the sheer volume and breadth of content that can add value when translated into multiple languages. Companies acquire and share content in many languages and formats, and scaling translation to meet needs is a tall order due to multiple document formats, integrations with optical character recognition (OCR), and the need to correct for domain-specific terminology.

Our goal is to simplify translation services, while enabling flexibility and control for our customers’ unique needs across industries. Read on to learn more about recent features and updates. 

Formatting matters: Document Translation is now GA 

In many cases, the layout of a document dictates how it should be interpreted—e.g., readers navigate text and discern meaning based on formatting, like bold or italicized text, or markups for headers, paragraphs, and columns. Previously, to automate translation of documents, text needed to be separated from these layout attributes, meaning the document’s structure was either lost or needed to be recreated later in the developer pipeline, after the text had been translated. This required translation teams to do a lot of extra work and maintain a lot of additional code. But now, those steps are unnecessary. Formatting can be retained throughout the translation process, handled directly by the Translation API Advanced. 

This feature lets customers translate documents in 100+ languages and supports document types such as Docx, PPTx, XLSx, and PDF while preserving document formatting.

And if your needs go beyond Document Translation, we can help you translate audio as well. For real-time streaming translation, check out the Media Translation API, and for offline transcription translation, combine the Translation API with the Video Intelligence API.   

Real-Time translation when you need it, Batch when you don’t

One of the biggest differentiators for Translation API Advanced’s document translation capabilities is the ability to do real-time, synchronous processing for a single file. 

For example, if you are translating a business document such as HR documentation, online translation provides flexibility for smaller files and provides faster results. You can easily integrate with our APIs via REST or gRPC with mobile or browser applications, with instant access to 100+ language pairs so that content can be understandable in any supported language. 

Meanwhile, batch translation allows customers to translate multiple files into multiple languages in a single request. For each request, customers can send up to 100 files with a total content size of up to 1 GB or 100 million Unicode codepoints, whichever limit is hit first.

State of the Art (SOTA) accuracy, with flexibility for customization

In order to achieve the highest level of accuracy for your translation, we now support multiple options:

• Use Google’s SOTA translation models: Each year, Google heavily invests to improve the quality of our translations across Apps, Cloud APIs, and Chrome, as well to enable multilanguage answers in Search. A popular metric for automatic quality evaluation of Machine translation systems is the BLEU score, which is based on the similarity between machine translation and the reference translations that were generated by people. While we push out incremental improvements for individual models on a monthly cadence, there are also times where we make significant leaps. In the releases since 2019, we have improved our average BLEU score by 5pts on average across 100+ languages and 7pts on low resource languages.
• Leverage glossaries for specific terms and phrases: Glossary is our terminology control feature. It allows you to import source content to define preferred translations, such as product names or department names. Then, when calling the glossary in the API request, your preferred translations will be enforced. This will work for words as well as phrase translation.
• Pick a pre-trained model with model selection: If you create custom models for machine translation, we don’t think you should have multiple client libraries and multiple APIs to maintain in order for you to use the best model for your needs. Translation API Advanced now supports Model Selection. Pick your pretrained model or pick your custom ML model built on AutoML for any language pair you’ve created and use the same API and the same client library. 
• Build custom translation models with AutoML: AutoML Translation is a suite of ML products that enable you to build high quality models for your own use case or data, with limited-to-no ML expertise or coding required. Bring your past human-validated translations to improve translation specificity for your domain.

Keep localization local with Regional Endpoints

If you are a customer operating in the EU, we recently launched an endpoint specifically for EU regionalization. This is a configurable endpoint for customers to store and perform machine translation processing of customer data only in the EU multi region. For now, this only supports our pretrained translation models and glossary, but batch translations will be coming soon.

How Eli Lilly uses Cloud Translation to translate content globally

Historically, translations at Eli Lilly have been complicated: numerous translation vendors have been needed for different languages and organizations, all with their own processes and expectations. On top of that, translations have been costly and slow. 

To solve this, Eli Lilly took a codified approach to enable users and systems to spend less time and resources to safely generate quality translations. 

Learn more, and even catch a demo, from Thomas Griffin, Translation Tech Lead & Global Regulatory Architect for Eli Lilly.

Learn more 

• To get started using Cloud Translation – Advanced, complete the setup and then try the Translate text (Advanced edition) quickstart.
• Document Translation is priced per page. For more information, see pricing.

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.