BMW Group uses AWS ML solutions to drive personalized customer experiences
To personalize the customer experience, BMW Group uses data to power artificial intelligence (AI) and machine learning (ML) solutions from Amazon Web Services (AWS). All workflows must comply with stringent data privacy laws that vary among the 140 countries where BMW operates.
BMW Group’s solution
After obtaining permission from vehicle owners, BMW Group ingests data from its fleet of 2.7 million vehicles and stores it in a scalable data lake built on AWS. The company collects and manages petabytes of unstructured data—such as information from onboard devices, and vehicle sensors—in a scalable, single source of truth called the BMW Cloud Data Hub. The solution also uses live data to feed ML models that generate predictions and near-real-time business insights.
The company has modernized its analytics architecture using Amazon Athena for live monitoring of data. Engineers gain insights through near-real-time visualizations using Amazon QuickSight, which powers data-driven organizations with unified business intelligence at hyperscale. Using AWS solutions, BMW Group creates personalized products for customers, such as BMW Proactive Care, which alerts customers to their vehicle needs before problems arise. “It’s a fully serverless, scalable cloud infrastructure that we can adapt to our growing fleet,” says Jens Kohl, head of offboard architecture at BMW Group. “It’s built 100 percent by BMW Group engineers on AWS infrastructure.”
On top of the BMW Cloud Data Hub, BMW Group built its Connected AI Platform, which brings together DevOps engineers who specialize in infrastructure management and data scientists seeking to create ML models to enhance the customer experience. The Connected AI Platform uses the open-source ML framework Kubeflow to spawn jobs in Kubernetes clusters, as well as jobs in any other AWS service. For example, BMW Group scientists train ML models on anonymized data from drivers who opt in. They train and deploy models within the Connected AI Platform using Amazon SageMaker, which lets companies build, train, and deploy ML models for nearly any use case with fully managed infrastructure, tools, and workflows.
Benefits of using AWS
Using AWS, BMW Group standardizes the ingestion and use of data assets across more than 30 markets and 250 local systems. The company’s backend solution fields at least one billion requests per day from internal BMW Group clients, who can create simple visualizations themselves using Amazon QuickSight.
“This is the first time that we can really talk about machine learning and applying AI to all the foundation that we have built,” says Stefan Meinzer, BMW Group’s general manager of corporate performance management and advanced analytics sales. “We can better think about customer centricity. It’s all about making every decision point in sales and marketing, being steered by the same transparent view about customers’ needs.”
Headquartered in Germany, BMW Group is a multinational manufacturer of luxury vehicles.
Learn how BMW Group uses AWS to modernize its technology stack, centralize petabytes of data, and personalize the customer experience using artificial intelligence and machine learning.
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“It’s a fully serverless, scalable cloud infrastructure, which we can adapt to our growing fleet. It’s built 100 percent by BMW Group engineers on AWS infrastructure.” —Jens Kohl, Head of Offboard Architecture, BMW Group
BMW Group used solutions from AWS to modernize its technology stack, centralize petabytes of data, and personalize the customer experience using artificial intelligence and machine learning.