The magnitude and direction of wind significantly impacts airport operations, and Lufthansa Group Airlines are no exception. A particularly troublesome kind is called BISE: it is a cold, dry wind that blows from the northeast to southwest in Switzerland, through the Swiss Plateau. Its effects on flight schedules can be severe, such as forcing planes to change runways, which can create a chain reaction of flight delays and possible cancellations. In Zurich Airport, in particular, BISE can potentially reduce capacity by up to 30%, leading to further flight delays and cancellations, and to millions in lost revenue for Lufthansa (as well as dissatisfaction among their passengers).

Being able to predict this kind of wind well in advance lets the Network Operations Control team schedule flight operations optimally across runways and timeslots, to minimize disruptions to the schedule. However, predicting speed and magnitude can be incredibly difficult to model and thus to predict— which is why Lufthansa reached out to Google Cloud.

Machine learning (ML) can help airports and airlines to better anticipate and manage these types of disruptive weather events. In this blog post, we’ll explore an experiment Lufthansa did together with Google Cloud and its Vertex AI Forecast service, accurately predicting BISE hours in advance, with more than 40% relative improvement in accuracy over internal heuristics, all within days instead of the months it often takes to do ML projects of this magnitude and performance.

“Being impressed with Google’s technology and prowess in the field of AI and machine learning, we were certain that my working together with their expert, to combine our technology with their domain expertise, we would achieve the best results possible,“ said Christian Most, Senior Director, Digital Operations Optimization at Lufthansa Group.

Collecting and preparing the dataset

The goal of Lufthansa and Google Cloud’s project was to forecast the BISE wind for Zurich’s Kloten Airport using deep learning-based ML approaches, then to see if the prediction surpasses internal heuristics-driven solutions and gauge the ease of use and practicality of the deep learning approach in production.

Since deep learning-based techniques require large datasets, the project relies on Meteoswiss simulation data, a dataset consisting of multiple meteorological sensor measurements collected from several weather stations across Switzerland over the past five years. By using this dataset, we obtained data on factors like wind direction, speed, pressure, temperature, humidity and more, at a 10 min resolution, along with some information about the location of the weather stations, such as altitude. These factors, which we hypothesized to be predictive of the BISE, ended up carrying valuable signals, as we would see later.

This collected data was next subjected to an extensive cleaning and feature engineering process using Vertex AI Workbench, in order to prepare the final dataset for training. The cleaning phase included steps to drop the features, or rows, that contained too many missing values, or failed statistical tests for entropy, etc. Since the direction of wind is a circular feature (between 0 and 360 degrees), this column/feature was replaced with two features: the corresponding sine and cosine embedding. The dataset was then flattened such that the columns contained all the relevant features and sensor measurements from all the weather stations at a particular 10-minute interval.

Since the target variable — i.e,. BISE — was not directly available, we engineered a proxy target variable for BISE called “tailwind speed around runway,” which above a certain threshold indicates the presence of BISE along the runway.

Forecasting wind in the Cloud

Once the dataset was ready, Lufthansa and Google Cloud evaluated several options before deciding to experiment and tune Vertex AI Forecast, Google’s AutoML-powered forecasting service, in order to achieve optimum results. Vertex Forecast is capable of the required feature engineering, neural architecture search, and hyper parameter tuning, and it is managed by Google Cloud to score in the top 2.5% in the M5 Forecasting Competition on Kaggle, in a completely automated fashion. These qualities made it an excellent choice for Lufthansa, to reduce the manual overhead of creating, deploying, and maintaining top performing deep learning models.

The raw data files were loaded from cloud storage, preprocessed on Vertex AI Workbench. Then, a training pipeline was initiated on Vertex AI Pipelines, which performed the following steps in sequence:

The .csv data file was loaded from Cloud Storage into a Vertex AI managed dataset.

A Vertex AI forecasting training job was initiated with the dataset, and it was also registered as a model in the Vertex AI Model Registry.

Upon completion, the model was evaluated on the test set, and the model’s predictions and the input features and ground truth of the test set, were stored in a user-defined table in BigQuery. Several test metrics were also available on the service and model dashboards.

One of the biggest challenges was the severe imbalance in the dataset, as measurements with BISE were very far and few in between. In order to account for this, instances where BISE occurred, as well the occurrences temporally close to them, were upweighted using weights calculated with methods including Inverse of Square Root of Number of Samples (ISNS), Effective Number of Samples (ENS), and Gaussian reweighting. The formulas for the methods are given below. These weights were supplied as separate columns in the dataset, and were iteratively used thereafter by the service as the “weight” column.

ISNS

ENS

Weighted gaussian

Results and next steps

In the above figures, the x-axis represents the forecast horizon and the Y-axis shows the respective metrics (Recall/F1-score). As shown after multiple experiments, we can see Vertex AI Forecast achieved higher recall and precision t (red bar), outperforming Lufthansa’s internal baseline heuristics, with the performance gap widening steadily as the forecast horizon extends further into the future. At the two-hour mark, our custom-configured Vertex AI Forecast model improved by 40% relative to the internal heuristics and 1700% compared to the random guess baseline. As we saw with other experiments, at a six-hour forecast horizon, the performance gap widens even more, with Vertex AI Forecast in the lead. Since forecasting BISE a few hours in advance is very beneficial to prevent flight delays for Lufthansa, this was a great solution for them.

“We are very excited to be able to not only do accurate long term forecasts for the BISE, but also that Vertex AI Forecasting makes training and deploying such models much easier and faster, allowing us to innovate rapidly to serve our customers and stakeholders in the best possible manner,” said Swiss Oliver Rueegg, Product Owner, Swiss International Airlines.

Lufthansa plans to explore productionizing this solution by integrating it into their Operations Decision Support Suite, which is used by the network controllers in the Operations Control Center in Kloten, as well as to work closely with Google’s specialists to integrate both Vertex AI Forecast and other of Google’s AI/ML offerings for their use cases.

Each day, more documents are created and used across companies to make decisions. However, the value in these documents is primarily expressed as unstructured data, which makes the value difficult and manually intensive to extract and use for business processes. 

As the number and variety of documents used by businesses proliferate, machine learning (ML) solutions need to be more flexible to handle the broader set of use cases. That’s why we introduced the first Document AI Workbench model, Custom Document Extractor (CDE), in Public Preview at Google Cloud Next ‘22. CDE makes it fast and easy to apply ML to virtually any document-based workflow to extract structured data from unstructured document types, to automate business processes. 

CDE lets developers and analysts use their own data to train models and extract fields from documents needed for the business. CDE lets organizations build models faster and with less data — thus accelerating time-to-value for processing and analysis of data in documents.

Today, we announce that Document AI Workbench is Generally Available (GA), open to all customers, ready for production use through APIs and the Google Cloud Console. Document AI Workbench is covered by the Document AI SLA — online and batch document prediction is supported with >=99.9% uptime. Furthermore, Document AI Workbench is now covered by Google Cloud’s GA product terms. For example, Google will notify customers at least 12 months before significantly modifying a customer-facing Google API in a backwards-incompatible manner.

In this blog post, we’ll explore ways customers are already using CDE and Document AI Workbench’s updated capabilities.

What users are saying about Workbench

Deliver higher model accuracy with Workbench

Users leverage Workbench to ultimately save time and money. A third party evaluated Document AI Workbench and concluded that it extracts data more accurately¹ than several competing products for document types with variable layouts (e.g. invoice, receipt, bank statements, paystubs). Better accuracy drives higher automation rates, helping Workbench users save time and money.  

Chris Jangareddy, managing director for Artificial Intelligence & Data at Deloitte Consulting LLP said, “Google Cloud Document AI is a leading document processing solution packed with rich features like multi-step classify and text extraction to automate sorting, classification, extraction, and quality assurance. By combining Document AI with Workbench, Google Cloud has created a forward-thinking and powerful AI platform for intelligent document processing that will allow for process transformation at an enterprise scale with predictable outcomes that can benefit businesses.”

Mansoor Khan, CEO of OneClinic said, “We help medical professionals scale their clinics through automation. We used Google’s Document AI Workbench to create a model to automatically extract data from patients’ insurance cards as part of our patient check-in software. Workbench is easy to use and we are really happy with the model accuracy — it extracts data more accurately than what we would expect from human data entry.”

Rajnish Palande, VP, Google Business Unit for BFSI, TCS said, “The Google Cloud Document AI Workbench leverages artificial intelligence (AI) to manage and glean insights from unstructured data. The Workbench brings together the power of classification, auto-annotation, page-number identification and multi-language support to help organizations rapidly deliver enhanced accuracy, improved operational efficiency, higher confidence in the information extract, and increased return on investment.” 

Build production ready models faster with Workbench

Document AI Workbench helps users create machine learning models faster. For example, a third-party evaluation shows Document AI Workbench trains machine learning models up to 3x faster than a leading competitor. This is an important improvement which lowers total cost of ownership and increases value.

Dallas Dolen, Partner, Google Alliance Leader at PwC said, “Google Document AI Workbench helps to accelerate our custom parser models training as well as improves accuracy and performance using a custom document extractor with human in the loop. It helps us solve complex business problems for our clients in the financial services and healthcare industries.”

Ziang Jia, Senior DocAl Development Lead at Resultant, said, “Document AI Workbench has unlocked a brand-new machine learning development experience for information extraction solutions. Its simplicity and robustness enabled us to build models and deliver a highly accurate outcome in an agile way for a large government agency. We couldn’t be more impressed by its simplicity and robustness and are excited to see how the product will evolve in the future.”

Sean Earley, VP of Delivery Services of Zencore said, “Document AI Workbench allows us to develop highly accurate document parsing models in a matter of days. Our customers have automated tasks that formerly required significant human labor. For example, using Document AI Workbench, a team of two trained a model to split, classify and extract data from 15 document types to automate Home Mortgage Disclosure Act reporting. The mean trained model accuracy was 94%, drastically reducing the operational cost of our customer’s compliance reporting procedures.”

What’s new with Document AI Workbench 

The latest Workbench capabilities make it even easier to train and deploy an extraction model: 

• With Workbench’s public APIs, you can programmatically create, delete, train, evaluate and deploy models.
• Our updated dataset management tools automatically detect and create existing schema labels from your pre-annotated documents. They also provide you more flexibility when creating and managing schema.
• Our new DocAI Toolkit includes a labeled document converter so that you can easily convert your labeled documents to DocAI’s format and start training faster.
• We’ve reduced the cognitive load for labelers with efficiency enhancements to our Labeling UI.    
• The revamped Processor Gallery helps you quickly identify the best model for your use case.

 What’s next for Document AI Workbench

We continue to invest in Document AI Workbench to help you automate document processing. Here are a few things we’re working on that we’re excited about:

• Classify document types with the Custom Document Classifier (CDC), coming soon in public preview
• Copy processor versions across projects and processors to streamline managing development and production environments
• Support larger documents (e.g., longer than 50 pages) so you can process a wider array of documents
• Broader (non-latin) language support–equivalent to Document AI OCR
• And many more investments, using state of the art technology, to help you build world class models faster to automate document processing

Document AI Workbench is in GA and ready for production workloads. Learn more via Document AI Workbench documentation or try it out in the Google Cloud Console.

^{Acknowledgements: Tomas Moreno, Outbound Product Manager, Lukas Rutishauser, Software Engineering Manager, Michael Kwong, Software Engineering Manager, Rajagopal Janani, Software Engineering Manager, Michael Lanning, UX Designer.}

^{1. When trained with 200+ documents}

Anyone who has ever coached or played on a sports team, or had a child involved in sports, knows how difficult scheduling and logistics can be. From game and practice schedules, to uniforms and who’s bringing the snacks, it can be a lot for coaches, administrators, parents, and players to manage.

It’s no wonder that TeamSnap, a sports team, club, and tournament management app, has exploded in popularity worldwide. By syncing events to everyone’s personal calendars and providing messaging and payment tracking, TeamSnap makes communication and organization easy.

TeamSnap markets its app to coaches, players, and clubs via targeted YouTube ads. It also uses Google AdWords and DoubleClick to advertise on search results and run programmatic campaigns. These methods have been highly effective, helping TeamSnap grow to millions of users worldwide and become one of the most popular apps in the iOS app store.

As its business and data grew, TeamSnap was challenged to track ROI and measure the customer journey across channels and devices over time. The company’s marketing budget grew quickly, making it even more important to spend wisely. With data in Google Analytics 360, DoubleClick Campaign Manager, Google AdWords, and Salesforce, TeamSnap needed a way to link and correlate those data sources in a scalable, timely, and cost-effective way to understand the true impact of its digital marketing across websites and mobile apps.

To avoid the painstaking manual process of pulling data from multiple sources, TeamSnap began using Google Analytics 360, which integrates with Google BigQuery, to provide a fully managed big data analysis service. TeamSnap analyzes the data using Tableau, which connects directly to Google BigQuery for fast analytics and helps the company share and collaborate on that information with self-service ease.

The combination allows TeamSnap to easily track the activity of millions of users with self-service ease, without worrying about the scalability or availability of the big data platform.

“Before Google Analytics 360, Google BigQuery, and Tableau, tracking our return on ad spend was difficult because we had so much data,” says Ken McDonald, Chief Growth Officer at TeamSnap. “We didn’t always have insights to make the best choices. We don’t have that problem anymore because we’ve moved to real-time reporting. We find additional revenue growth opportunities almost daily.”

Making Ad Dollars Work Harder

TeamSnap now automatically imports unsampled Google Analytics 360 logs into the Google BigQuery data warehouse. To import data from other sources such as Google AdWords, DoubleClick, and YouTube, TeamSnap uses Google BigQuery Data Transfer Service. With all relevant data consolidated in Google BigQuery, TeamSnap can use Tableau to perform advanced analytics on its digital marketing, executing ad-hoc analyses in seconds, while eliminating data sampling issues, to improve accuracy. These analyses can also be reused and shared with internal and external stakeholders via Tableau Online, promoting governed reuse and consistency.

“Using Google Analytics 360 and Google BigQuery with Tableau to track our return on ad spend is ideal,” says Ken. “It’s easy to use SQL to query the data or explore it with drag-and-drop ease.”

With Google BigQuery, Ken and his team can bring all the data from the TeamSnap billing systems, internal CRM, and other Google services into one straightforward dataset that everyone uses. With Tableau, users are able to perform self-service analytics on this data and provision analyses via shared dashboards that communicate the same consistent truth across the company. These dashboards provide a single view of the business to discover new patterns and questions worth analyzing.

All of this results in enormous time savings because no one is re-inventing the wheel. “Using these tools, we immediately reallocated $300,000 of ad spend that was performing poorly, generating 200% ROI in the first two days,” says Ken.

Ken now spends his time analyzing data instead of trying to pull it all together, identifying pockets of inefficient spend in real time and reallocating those marketing dollars toward better performing campaigns.

“Before, we could only focus on the largest campaign-level datasets because it was so time consuming to pull the data,” he says. “With Google BigQuery and Tableau, we can examine our advertising ROI much more granularly and reallocate more than $10 million in ad spend annually to grow the company faster and more efficiently.”

More Effective A/B Testing

To make sure it is delivering the best customer experiences, TeamSnap uses Google Optimize to run A/B tests on its website. It uses Google BigQuery and Tableau to verify and supplement these findings by measuring longer-term customer behavior across devices, spanning both web and mobile apps.

By pulling in data from Google Optimize, Google Analytics 360, Salesforce, and in-house billing and CRM systems, and understanding it with Tableau, TeamSnap has increased the accuracy and effectiveness of its A/B testing, gaining a more complete picture of customer onboarding and activity. In some cases, it found that short-term indicators it previously trusted were actually poor predictors of long-term behavior.

“Integration between Google Analytics 360 and Google BigQuery is seamless, giving us much more confidence in our A/B testing,” says Ken. “We’re constantly finding new and interesting ways to use our digital marketing data. Often, making a simple change can increase revenue by hundreds of thousands of dollars a year.”

Improving Product Quality

TeamSnap also uses Google BigQuery and Tableau to improve its own product, tracking customer activity at such a granular level that usability and functionality issues can be exposed and addressed faster. It’s also increasing customer engagement by verifying that potential customers are coming in through the right onboarding path—for example, a coach versus a player, or a consumer versus a club or other sports business. Using A/B testing to make sure customers are routed to the appropriate flow, TeamSnap drove $4 million in additional customer value each year.

“We initially chose Google BigQuery and Tableau to help with marketing, but we realized quickly that they could help us on the product side as well,” says Ken. “Most of the testing we do is about making things better and easier for our customers, and we’re accelerating that process with Google BigQuery and Tableau.”