Once the AI Studio’s muscles started to build, the conversation at Lufthansa turned to modernizing the company’s data science platform to bring all the disparate projects under one virtual roof—boosting the cache and effectiveness of its data science group and tying their activities closer to the needs of the business.

Data scientists and data engineers often struggle with having to spend too much time maintaining their projects and not enough time proving their business value. At Lufthansa, all of the above was true, and it was also compounded by limited scalability, lack of access to public software updates and a need for security improvements. What the company needed was a tool inside the data science pipeline to monitor, build and scale models.

IBM® Data Science and AI Elite and IBM Software Services teams joined Lufthansa in a two-day Enterprise Design Thinking™ Workshop to build out a data science platform that would offer a single environment where data scientists could experiment with new techniques and quickly roll out models with monitoring and modeling already in place.

The data science platform allows data scientists to work with new data sources. Or, by virtue of being open source, they can work more collaboratively or in their preferred language—or take advantage of other data science capabilities in IBM Watson Studio, such as AutoAI and IBM Watson Machine Learning technology for model development and deployment. Together with the IBM Watson OpenScale solution, used for bias and drift mitigation during runtime, all of the offerings are available as platform as a service (PaaS) and software as a service (SaaS) options on the IBM public cloud or as microservices through the IBM Cloud Pak® for Data platform available on any cloud.

Over a 10-week engagement, the Data Science and AI Elite team set up a new operational workflow to support the development of new data science projects using the IBM Watson Studio and IBM Watson Machine Learning solutions to create an open platform on a public cloud using PaaS and SaaS. This gave Lufthansa scalability and flexibility to handle mission-critical workloads and accelerate the deployment of those projects in production.

Lufthansa data scientists worked with the Data Science and AI Elite team to prototype three use cases to help the airline run smarter and more efficiently—helping avoid delays, better predict boarding times and avoid long queues at check-in counters.

In less than two years, the airline has quickly moved from AI proof of concepts (PoCs) to scaling data science projects further into the organization, moving past constraints such as how much test data it could include in its models. Lufthansa did it thanks to a partnership with IBM that brought forth deep expertise and solutions inherent in IBM’s prescriptive method, the AI ladder—together with the airline’s migration of AI services to IBM Cloud®.

The Lufthansa data science team can now develop new use cases in IBM Watson Studio while making improvements to the old ones. In addition to the three use cases, Lufthansa data scientists can now push out other projects—mostly to further increase passenger experience or to support operational or strategic decisions from employees.

Founded in 1953 and based in Cologne, Lufthansa (link resides outside of ibm.com) is the largest airline in Germany. The airline’s nearly 140,000 employees serve 220 destinations around the world, and it reported revenues of EUR 36 billion in 2019.