Standardizing MLOps at scale for CARIAD

The Challenge

CARIAD SE, Volkswagen Group's software powerhouse, faced a critical challenge that many enterprise organizations encounter: how to operationalize numerous machine learning projects efficiently and consistently. With multiple ML initiatives running across different teams, CARIAD needed a standardized MLOps approach that could scale across their organization while maintaining quality, reliability, and governance standards.

The existing landscape was fragmented, with each team developing their own approaches to model deployment, monitoring, and lifecycle management. This led to inconsistencies, duplicated efforts, and difficulties in maintaining production ML systems at scale.

Our Solution: The FTI Framework Implementation

HCON partnered with CARIAD to implement a comprehensive MLOps solution built on Databricks, leveraging the proven FTI (Feature-Training-Inference) approach. This methodology breaks down machine learning projects into three distinct, manageable pipelines:

• Feature Pipeline: Handles data preprocessing and feature engineering

• Training Pipeline: Manages model development, training, and validation

• Inference Pipeline: Orchestrates model deployment and prediction serving

  
  

Each pipeline operates as a standalone project with its own development lifecycle, enabling teams to implement proper software engineering practices including code versioning, unit testing, integration testing, monitoring, performance observability and automated deployment.

Technical Architecture

Our implementation established two critical communication layers:

• Feature Store (Azure Blob Storage): Serving as the central hub where the feature pipeline writes processed features and the training pipeline reads its inputs. This ensures consistent feature definitions across all models and enables feature reuse across projects.

• Model Repository (MLflow): Acting as the centralized model registry where training pipelines store validated models and inference pipelines retrieve them for deployment. This provides full model lineage, versioning, and governance capabilities.

The Template-First Approach

Rather than building individual solutions for each team, we developed a comprehensive template that embodies the FTI methodology and MLOps best practices. This template serves as a foundation that teams can quickly adopt and customize for their specific use cases, dramatically accelerating their time-to-production.

The template includes:

• Pre-configured pipeline structures for all three components

• Integrated testing frameworks and CI/CD workflows

• Standardized logging and monitoring capabilities

• Documentation and onboarding guides

Results & Impact

The standardized MLOps framework has transformed how CARIAD approaches machine learning operationalization:

• Accelerated Development: Teams can now onboard new ML projects in days rather than weeks

• Improved Consistency: All projects follow the same architectural patterns and quality standards

• Enhanced Collaboration: The standardized approach enables better knowledge sharing across teams

• Scalable Operations: The template-based approach allows CARIAD to scale ML operations efficiently across the organization

• Reduced Maintenance Overhead: Centralized patterns and tools minimize the operational burden on individual teams