Deutsche Bahn runs a shared AI platform on Amazon SageMaker Studio for over 100 developers
Deutsche Bahn· GermanyAmazon SageMakerDeutsche Bahn's AI platform team (DB Systel) operates Amazon SageMaker Studio as a group-wide platform where multiple data analytics teams develop, train and run ML workloads, for use cases ranging from railway maintenance and forecasting to future generative AI applications. Each team requests a SageMaker domain through a self-service portal, and the VPC has no outbound internet connectivity. A product owner says that within 8 weeks over 120 developers were onboarded and 25 SageMaker domains provisioned; the post's conclusion cites over 100 developers and 20 domains.
Overview
Deutsche Bahn's AI platform team (DB Systel) operates Amazon SageMaker Studio as a group-wide platform where multiple data analytics teams develop, train and run ML workloads, for use cases ranging from railway maintenance and forecasting to future generative AI applications. Each team requests a SageMaker domain through a self-service portal, and the VPC has no outbound internet connectivity. A product owner says that within 8 weeks over 120 developers were onboarded and 25 SageMaker domains provisioned; the post's conclusion cites over 100 developers and 20 domains.
This entry has 13 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Before the shared platform, each Deutsche Bahn data analytics team managed and operated its own JupyterLab notebooks, which was not efficient or cost-effective; the organization needed a scalable platform that meets compliance and security standards.
The solution
A dedicated AI platform team manages Amazon SageMaker Studio from a central platform account. Each data analytics team requests one or more SageMaker domains through an internal self-service portal; an orchestration workflow provisions the domain, an Azure AD group, an IAM role, an S3 bucket and a customer managed key through a CI/CD pipeline deploying an AWS CDK app. The VPC has no outbound internet connectivity, and lifecycle configuration shuts down idle instances to reduce unnecessary costs.
Reported business value
According to the DB Systel product owner, SageMaker Studio provided a common platform that is scalable and security compliant, and within 8 weeks over 120 developers were onboarded and 25 SageMaker domains provisioned. The post's conclusion cites over 100 developers and 20 domains in a single AWS account, and names high scalability through automation and self-service plus a consumption-based pricing model as major benefits.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other transportation entries in the register.
How Schiphol is leveraging tech, design, data and AI-powered intelligence to redefine airport capacity and flow management
Royal Schiphol Group built in-house AI-powered tools — Dynamic Time Slots, (Passenger) Flow Balancing, and Gate Planning Insights — to align passenger demand with security and gate capacity. Dynamic Time Slots lets passengers pre-book security check times, steering an average of 7.5% of passengers away from peak moments (up to 22% on critical days), with 99.6% of passengers waiting under 10 minutes. Flow Balancing uses real-time sensor data, predictive models and simulation (a digital twin) to steer passenger groups, preventing over 4,000 minutes of crowding in 2024. Gate Planning Insights uses machine learning to optimise gate stand planning and turnaround operations. Overall passenger satisfaction score is 4.4 out of 5.
Royal Schiphol GroupDigital Twin Simulation · Private 5G Network · Real-time Sensor DataAirAsia drives 97% invoice posting accuracy
AirAsia implemented Oracle Fusion Cloud ERP with AI-based invoice data capture, duplicate detection, supplier validation, approvals, posting, and exception review in a single platform for its accounts payable operations. For its initial top-nine-vendor group, invoice processing cycle time fell by 80%, invoice posting accuracy reached 97%, manual data entry effort dropped by 80%, and more than 30% of routine AP tasks became fully automated. AirAsia is positioned to evaluate Fusion Agentic Applications, including Payables Agent and Payments Agent, to further automate exception handling.
AirAsiaOracle Fusion Cloud ERPHapag-Lloyd enhances corporate audit efficiency with GenAI on Databricks
German shipping company Hapag-Lloyd fine-tuned Databricks' open-source DBRX model and built a RAG chatbot to automate audit finding generation and executive summaries, cutting review time per finding by 66% and executive summary review time by 77%.
Amphitrite Rides AI Wave to Boost Maritime Shipping, Ocean Cleanup With Real-Time Weather Prediction and Simulation
France-based startup Amphitrite fuses satellite data and AI to simulate and predict oceanic currents and weather, using the NVIDIA AI and Earth-2 platforms. Its fine-tuned, three-kilometer-scale AI models (dubbed ORCAst), trained on NVIDIA GPUs, analyze ocean current, wave, and wind data to help ships optimize routes and reduce fuel consumption and carbon emissions. Amphitrite trains and runs its AI models using NVIDIA H100 GPUs on premises and in the cloud, building on the FourCastNet model from Earth-2. Fusing AI and satellite imagery, Amphitrite can improve the accuracy of global ocean current analyses by up to 2x compared with traditional methods. A case study along the Mediterranean Sea found the NVIDIA-powered Amphitrite fine-scale routing solution helped reduce one shipping line's carbon emissions by 10%. Shipping and oceanographic companies using Amphitrite's solutions include CMA-CGM, Genavir, Louis Dreyfus Armateurs, and Orange Marine; the company also works with an NGO to track and remove plastic pollution in the Pacific Ocean.
Was this helpful?
Your feedback helps us improve our use case database