CI/CD Pipelines for Software-Defined Vehicles (SDVs) Incorporating MLflow

Introduction

Software-Defined Vehicles (SDVs) are modern cars that rely on centralized computing and software for key functions, enabling over-the-air (OTA) updates, AI-driven automation, and cloud connectivity. Existing SDVs include Tesla’s lineup, Rivian R1T, Lucid Air, Porsche Taycan, and Ford’s F-150 Lightning, all offering software-based performance enhancements and autonomous features. Automakers like BMW, Volkswagen, GM, and Mercedes-Benz are integrating SDV technology into future models, while newcomers like Sony-Honda Afeela and Apple’s rumored car aim to revolutionize mobility with software-first designs. Future vehicles, such as Toyota’s Arene platform and VW’s Trinity project, will focus on AI, real-time data, and digital experiences, transforming the automotive industry into a cloud-driven, continuously evolving ecosystem.

The rise of Software-Defined Vehicles (SDVs) is transforming the automotive industry by making software a core component of vehicle functionality. Unlike traditional automobiles, where hardware dictates capabilities, SDVs rely on software updates to enhance performance, introduce new features, and improve safety. This shift necessitates robust Continuous Integration and Continuous Deployment (CI/CD) pipelines to manage ongoing software updates, particularly those involving machine learning (ML) models.

This article explores the integration of MLflow into CI/CD pipelines for SDVs, addressing key challenges and best practices. We also examine the impact of SDVs on the automotive supply chain, the role of sensors, emitters, and communication subsystems, and strategies for ensuring secure, efficient, and scalable deployment.

Key Technologies Enabling SDVs

• Centralized computing (vs. legacy ECUs)
• Over-the-Air (OTA) updates for software improvements
• Cloud connectivity for real-time data exchange
• AI-driven automation & driver assistance
• Digital cockpit & software-defined user experiences

SDVs are shaping the future of mobility, with a shift from hardware-defined vehicles to cloud-based, AI-driven platforms.

The Expanding Role of Software in SDVs

The Need for CI/CD Pipelines in SDVs

In SDVs, software governs functionalities such as infotainment systems, advanced driver-assistance systems (ADAS), and autonomous driving features. Unlike traditional vehicles that require physical upgrades for enhancements, SDVs can receive over-the-air (OTA) updates, improving safety and performance without hardware modifications. However, this requires a reliable CI/CD pipeline that can:

• Automate testing and deployment of software updates
• Manage ML model lifecycle and integration
• Ensure compliance with safety and security standards
• Facilitate collaboration between multiple suppliers

MLflow Integration in CI/CD Pipelines

MLflow is an open-source platform designed to manage the end-to-end machine learning (ML) lifecycle, including experimentation, reproducibility, deployment, and model tracking. In the context of Software-Defined Vehicles (SDVs), MLflow plays a crucial role in the CI/CD pipeline by enabling efficient version control of ML models, automated testing, and seamless deployment of AI-driven features such as autonomous driving, predictive maintenance, and driver assistance systems. Its integration ensures that ML models powering SDV functionalities are continuously monitored, improved, and updated via over-the-air (OTA) updates, enhancing vehicle performance and safety while maintaining compliance with evolving regulatory standards.

MLflow is an open-source platform that simplifies ML lifecycle management by offering:

1. Experiment Tracking: Logs ML model parameters, metrics, and artifacts, allowing data scientists to compare and refine models.
2. Reproducibility: Ensures ML models can be consistently replicated and deployed across different environments.
3. Model Deployment: Streamlines integration with existing software systems, reducing deployment friction.
4. Performance Monitoring: Continuously tracks model performance, enabling proactive updates and maintenance.

Integration of MLflow in CI/CD Pipelines

A multi-vendor Software-Defined Vehicle (SDV) CI/CD pipeline must accommodate diverse hardware and software ecosystems while ensuring seamless integration, validation, and deployment of software updates across different vehicle platforms. The pipeline typically includes several key stages: source code management, build automation, containerization, hardware-in-the-loop (HIL) testing, simulation-based validation, continuous monitoring, and over-the-air (OTA) deployment. Given the complexity of SDVs, the pipeline should support multi-cloud infrastructure, heterogeneous software stacks, and real-time data processing. Additionally, robust security, compliance checks, and fail-safe rollback mechanisms are essential to prevent software failures from compromising vehicle safety.

Recommended Tools for Multi-Vendor SDV CI/CD

1. Version Control: Git, GitHub, GitLab, Bitbucket
2. Build Automation: Gradle, Maven, Bazel, CMake, Buildroot
3. Containerization & Orchestration: Docker, Podman, Kubernetes
4. Simulation & Testing: CARLA, NVIDIA DRIVE Sim, MATLAB Simulink, Hardware-in-the-Loop (HIL) frameworks
5. CI/CD Orchestration: Jenkins, GitHub Actions, GitLab CI/CD, ArgoCD
6. Machine Learning Model Management: MLflow, TensorFlow Extended (TFX), Kubeflow
7. Security & Compliance: Fortify, SAST (SonarQube), Dependency-Track DAST (OWASP ZAP), SBOM tools (CycloneDX)
8. Monitoring & Observability: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana)
9. OTA Updates & Edge Deployment: Eclipse Hawkbit, AWS IoT Greengrass, Red Hat Edge

Comparison with Traditional CI/CD Pipelines

The SDV CI/CD pipeline introduces additional complexity due to its multi-vendor ecosystem, safety-critical requirements, and real-world testing constraints, making it distinct from traditional software CI/CD workflows.

Comparison with Traditional CI/CD Pipelines

Why Choose GitLab CI/CD or GitHub Actions Over Jenkins?

While Jenkins has been a staple in CI/CD, modern tools like GitLab CI/CD and GitHub Actions offer distinct advantages for SDVs. These platforms provide integrated solutions that facilitate collaboration across distributed teams, essential for tiered suppliers. They offer seamless integration with version control systems, enabling better traceability and management of code changes. Additionally, they support containerization and microservices architectures, which are increasingly prevalent in SDV development. Their cloud-native capabilities allow for scalable and flexible deployment, crucial for handling the diverse and dynamic nature of SDV software (LambdaTest, 2023).

Key Steps in MLflow Integration

1. Automate Experiment Tracking: Use MLflow to log and compare different model versions, ensuring optimal performance.
2. Integrate with Version Control: Connect MLflow with tools like Git to manage code and model changes effectively.
3. Deploy Models Automatically: Leverage MLflow’s deployment features to integrate ML models into SDV software seamlessly.
4. Monitor and Retrain Models: Continuously track ML model performance and retrain as needed to maintain accuracy.

Challenges in CI/CD for SDVs

Implementing CI/CD for SDVs presents unique challenges, including:

• Multi-Tier Supplier Collaboration: SDVs involve multiple suppliers contributing different software components, requiring seamless integration.
• Regulatory Compliance: Automotive software must comply with stringent safety and security standards.
• Machine Learning Complexity: ML models require continuous training, validation, and monitoring to ensure they perform reliably in real-world conditions.
• Security Risks: SDVs rely on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, necessitating robust cybersecurity measures.

Determining the Number of Machine Learning Pipelines

The number of ML pipelines required depends on supply chain complexity, the number of suppliers, and specific ML use cases. Multiple pipelines may be needed for tasks like demand forecasting, inventory optimization, and supplier risk assessment. Each pipeline should be tailored to its task, balancing coverage and system manageability. For instance, a complex supply chain with diverse products and markets may require more pipelines to address various operational needs (ScienceDirect, 2024).

Sharing ML Pipelines Across Suppliers

Sharing ML pipelines across suppliers can offer cost savings, improved collaboration, and consistent model performance. However, data privacy, security, and compatibility with suppliers’ systems must be considered. Standardized pipelines adaptable to different suppliers’ needs can facilitate sharing. MLflow ensures pipelines are accessible and manageable across the supply chain, promoting efficiency and collaboration (Ivalua, 2025).

Challenges of CI/CD for SDVs with Machine Learning

Integrating machine learning (ML) into the CI/CD pipeline for SDVs presents unique challenges. ML models require continuous training and validation, which can be resource-intensive and time-consuming. The pipeline must accommodate the iterative nature of ML development, where models are frequently updated based on new data. Ensuring the safety and reliability of ML components in a vehicle context is crucial, necessitating rigorous testing and validation processes. This complexity is heightened when multiple suppliers are involved, each with their own ML models and data requirements (ResearchGate, 2023).

Essential Sensors, Emitters, and Communication Systems in SDVs

Integration of SDV Components

SDVs rely on a complex network of sensors, emitters, and communication subsystems. These components must be seamlessly integrated into the CI/CD pipeline to ensure they function correctly with the software updates. This involves:

• Testing and Validation: Each component must be rigorously tested and validated within the pipeline to ensure compatibility and performance. Simulation environments can be used to replicate real-world scenarios (HERE Technologies, 2025).
• Real-Time Data Processing: SDVs generate vast amounts of data that need to be processed in real-time. MLflow can help manage this data by integrating with vehicle hardware and ensuring that machine learning models are optimized for real-time performance (TechCrunch, 2025).

Sensors

SDVs rely on a variety of sensors for navigation and decision-making:

• Cameras: Capture high-resolution images to detect lane markings, pedestrians, and other vehicles.
• Radar: Measures object distance and speed, especially useful in low-visibility conditions.
• LiDAR: Creates detailed 3D maps of the environment for accurate object detection and navigation.
• Ultrasonic Sensors: Detect close-range obstacles, aiding in parking and low-speed maneuvering.
• Inertial Measurement Units (IMUs): Monitor acceleration and rotational movement for vehicle stability.
• GPS Receivers: Enable precise vehicle tracking and navigation.
• Environmental Sensors: Measure temperature, humidity, and road conditions.

Emitters and Communication Subsystems

• Ultrasonic Emitters: Work with ultrasonic sensors to measure distance and avoid obstacles.
• OBD-II Systems: Provide real-time vehicle diagnostics and performance data.
• Vehicle-to-X (V2X) Communication: Facilitates communication with other vehicles, infrastructure, and pedestrians to enhance road safety.
• Wi-Fi and Bluetooth Modules: Enable OTA updates, remote diagnostics, and seamless connectivity with mobile devices.

Supplier Collaboration and Standardization

The Expanding Supply Chain

The shift towards SDVs is driving a significant increase in the number of suppliers within the automotive supply chain. According to a report by Dentons, the integration of advanced communication protocols, AI, and the Internet of Things (IoT) in connected vehicles is leading to strategic collaborations with tech firms (Dentons, 2025). This expansion is not only increasing the number of suppliers but also diversifying the types of suppliers involved, including those specializing in software development, cybersecurity, and data management.

Managing the Expanding Supply Chain

With SDVs, the automotive supply chain now includes software vendors, AI specialists, and cloud service providers. Effective supplier collaboration requires:

• Standardized APIs and Protocols: Ensuring seamless integration between different components.
• Shared CI/CD Pipelines: Using common tools like GitLab CI/CD and GitHub Actions to streamline development.
• Cloud-Based Collaboration Platforms: Facilitating real-time communication and coordination among suppliers.

Security Measures in CI/CD Pipelines

Security is critical in CI/CD pipelines for SDVs. Key security strategies include:

1. Encryption and Authentication: Protecting data in transit and preventing unauthorized access.
2. Vulnerability Scanning: Conducting regular security audits to identify and fix vulnerabilities.
3. Secure Coding Practices: Ensuring that software updates do not introduce new security risks.

Real-World Examples and Case Studies

A notable example of successful CI/CD implementation in the automotive sector is the collaboration between Volkswagen’s CARIAD and Bosch. They developed a CI/CD pipeline from scratch to support automated driving functions, demonstrating the potential of a well-integrated pipeline to facilitate innovation and efficiency across tiered suppliers (CARIAD, 2023). Such case studies provide valuable insights into the practical application of CI/CD in SDVs, highlighting the importance of strategic collaboration and advanced tooling.

Supplier Collaboration Strategies

Effective collaboration among tiered suppliers is essential for a shared CI/CD pipeline. Strategies such as adopting standardized communication protocols, utilizing collaborative platforms, and implementing shared repositories can enhance coordination. Tools like GitLab CI/CD and GitHub Actions offer integrated solutions that facilitate collaboration across distributed teams, essential for tiered suppliers. These platforms support seamless integration with version control systems, enabling better traceability and management of code changes (MathWorks, 2023).

Testing and Deployment Strategy for SDVs

Recommended Testing Approach

A robust testing strategy ensures SDV software reliability:

• Unit Testing: Verifies individual software components in isolation.
• Integration Testing: Ensures compatibility between different modules.
• System Testing: Validates end-to-end functionality.
• User Acceptance Testing (UAT): Confirms software meets user expectations.
• Regression Testing: Ensures updates do not break existing functionality.

Managing Final Integration with Multiple Suppliers

To integrate software contributions from various suppliers:

• Implement Version Control Systems: Track and manage software changes efficiently.
• Use Continuous Integration (CI): Automate integration and testing to detect issues early.
• Establish a Dedicated Testing Environment: Simulate real-world conditions before deployment.

Conclusion

SDVs represent the future of the automotive industry, enabling continuous software-driven innovation. However, their success depends on robust CI/CD pipelines that integrate ML models, streamline supplier collaboration, and maintain security. By incorporating MLflow, automotive companies can enhance model tracking, deployment, and monitoring, ensuring seamless and reliable software updates.

As SDVs evolve, so must the tools and strategies used to develop and maintain them. By adopting best practices in CI/CD, leveraging advanced testing methodologies, and fostering supplier collaboration, the automotive industry can pave the way for safer, smarter, and more efficient vehicles.

References

1. Restackio. (2024). Explore CI/CD practices using MLflow to streamline machine learning workflows and enhance model deployment efficiency.
2. GitLab. (2024). Build an ML app pipeline with GitLab Model Registry using MLflow.
3. Databricks. (2024). How does Databricks support CI/CD for machine learning?
4. Stackademic. (2024). MLOps: Building a CI/CD Pipeline for Machine Learning Models.
5. Medium. (2024). CI/CD for Machine Learning in 2024: Best Practices to Build, Test, and Deploy.
6. SentinelOne. (2024). CI/CD security tools.
7. Palo Alto Networks. (2024). What is the CI/CD pipeline and CI/CD security.
8. Cycode. (2024). CI/CD Pipeline Security: Best Practices Beyond Build and Deploy.
9. Red Hat. (2024). What is CI/CD security.
10. TechCrunch. (2025). CES 2025: Self-driving cars were everywhere, plus other transportation tech trends.
11. Continental. (2025). Continental advances mobility from road to cloud at CES 2025.
12. S&P Global. (2025). Automotive Suppliers Outlook for 2025: Trends and Challenges.
13. HERE Technologies. (2025). Autonomous driving features and trends 2025.
14. Digital.ai. (2025). CI/CD pipeline best practices.
15. Dentons. (2025). Trends and Challenges Shaping the Automotive Industry in 2025. Retrieved from Dentons.
16. Rinf.tech. (2025). Software-Defined Vehicles: Evolution, Challenges, and Future Outlook. Retrieved from Rinf.tech.
17. Data Science Journal. (2023). MLflow: An Open-Source Platform for the Machine Learning Lifecycle.
18. Tandfonline. (2024). The Impact of Artificial Intelligence on the Supply Chain in the Era of Data Analytics. Retrieved from Tandfonline.
19. Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Hong, M., Konwinski, A., … & Zeng, Q. (2018). Accelerating the machine learning lifecycle with MLflow. Databricks.
20. Enhancing supply chain management with deep learning and machine learning techniques: A review. (2024). ScienceDirect.
21. Top Supply Chain Management Strategies for 2025. (2025). Ivalua.
22. Continuous Integration and Continuous Deployment (CI/CD) in Machine Learning. (2021). IEEE Access.
23. How can the CI/CD pipeline be shared across tier 1 tier 2 and tier 3 suppliers?
24. CARIAD. (2023). “CARIAD’s Journey Towards the Software Defined Vehicle.” Retrieved from CARIAD Technology.
25. ResearchGate. (2023). “Impact of Emerging AI Techniques on CI/CD Deployment Pipelines.” Retrieved from ResearchGate.
26. MathWorks. (2023). “Accelerating Development for Software-Defined Vehicles Using CI/CD.” Retrieved from MathWorks.
27. LambdaTest. (2023). “CircleCI Vs. GitLab: A Comparative Analysis.” Retrieved from LambdaTest.
28. SentinelOne. (2023). “CI/CD Security Tools.” Retrieved from SentinelOne.
29. SmartBear. (2023). Software Testing Methodologies. Retrieved from https://smartbear.com/learn/automated-testing/software-testing-methodologies/
30. HeadSpin. (2023). Unit, Integration, and Functional Testing. Retrieved from https://www.headspin.io/blog/unit-integration-and-functional-testing-4-main-points-of-difference
31. QASource. (2023). Top 10 Testing Strategies to Ensure Code Quality. Retrieved from https://blog.qasource.com/top-10-testing-strategies-to-ensure-code-quality-in-software-engineering
32. Functionize. (2023). Acceptance Testing: A Step-by-Step Guide. Retrieved from https://www.functionize.com/automated-testing/acceptance-testing-a-step-by-step-guide
33. DeviQA. (2023). 20 Software Quality Assurance Best Practices. Retrieved from https://www.deviqa.com/blog/20-software-quality-assurance-best-practices/
34. ProcurementTactics. (2023). Supplier Integration: What is it? Retrieved from https://procurementtactics.com/supplier-integration/
35. Chacon, S., & Straub, B. (2014). Pro Git. Apress.
36. LaunchDarkly. (2023). Software Development Tools. Retrieved from https://launchdarkly.com/blog/software-development-tools/
37. MasterofCode. (2023). Three Phases Deployment Testing Cycle. Retrieved from https://masterofcode.com/blog/three-phases-deployment-testing-cycle
38. Jamsa, K. (2013). Cloud Computing: SaaS, PaaS, IaaS, Virtualization, Business Models, Mobile, Security and More. Jones & Bartlett Learning.
39. McGraw, G. (2006). Software Security: Building Security In. Addison-Wesley.
40. Rubin, K. S. (2012). Essential Scrum: A Practical Guide to the Most Popular Agile Process. Addison-Wesley.
41. Farley, D. (2016). Continuous Delivery Pipelines: How to Build Better Software Faster. Pearson.