Quick Highlights
25%
Infrastructure Cost Reduction
30–40%
Faster Model Development
Unified
Single Platform, All Teams
Full
Governance & Data Lineage
The Challenge
Across a global automotive enterprise running multiple AI programmes simultaneously, the absence of a shared platform was creating compounding operational and financial consequences.
- Fragmented environments across divisions — duplicated workloads, inconsistent outputs, and infrastructure expenditure that could not be effectively attributed or managed.
- No model lifecycle governance — no reliable basis for auditing model performance, data lineage, or deployment history across the organisation.
- Unmanaged infrastructure spend — distributed compute and storage with no tagging or cost attribution framework, making optimisation effectively impossible.
- Collaboration constraints — no standardised access control or shared tooling, creating friction between data engineering and data science functions.
The Ascentt Solution
Ascentt designed and implemented a scalable Data Science Platform on AWS, integrating Databricks and Amazon SageMaker into a single governed environment spanning data ingestion through to model deployment and ongoing monitoring.
- Databricks on AWS — distributed data processing, collaborative notebook environments, and job orchestration for engineering and analytics workloads.
- Amazon SageMaker — end-to-end ML lifecycle management: experimentation, training, tuning, deployment, and inference at scale.
- Amazon API Gateway and AWS Lambda — secure, API-based model serving with granular access control enforced at the infrastructure level.
- AWS IAM, WAF, and CloudWatch — role-based permissions, threat mitigation, and continuous monitoring of job health, resource utilisation, and cost.
- Amazon S3, CloudFront, and Route 53 — integrated storage, global content delivery, and DNS management.
CI/CD automation, cost governance frameworks, and end-to-end tagging policies ensure every dataset, model, and job run is auditable and correctly attributed. Engagement scope spanned architecture design through deployment and post-implementation optimization.
The Results
The platform consolidated a fragmented operating environment into a single governed system — reducing costs, improving development velocity, and providing leadership with the visibility needed to manage AI investment with confidence.
METRIC | OUTCOME |
Infrastructure Costs | Reduced by over 25% through workload optimisation and resource right-sizing across Databricks and SageMaker. |
Model Development Speed | Accelerated by 30–40% through standardised environments and CI/CD pipeline automation. |
Operational Visibility | Centralised monitoring of job performance and resource consumption across all workloads, established for the first time. |
Governance and Lineage | End-to-end data lineage, role-based access control, and full audit capability across the data and model lifecycle. |
Team Collaboration | Data engineers and scientists operating in a shared environment, eliminating duplicated effort across divisions. |
AI Scalability | New initiatives onboarded within a standardised framework, with measurable improvements in delivery reliability and cost predictability. |
Strategic Implications
Fragmented data science infrastructure is one of the most consistent barriers to scaling AI beyond isolated use cases — and one of the most frequently underestimated. Teams build capability in silos, infrastructure costs accumulate without visibility, and governance gaps create audit exposure that compounds as the number of models in production grows. The 30 to 40 per cent improvement in development velocity here is a direct consequence of removing that structural friction, not of adding new capability.
For enterprise leaders evaluating AI infrastructure, the relevant question is not whether a unified platform delivers value, but what the current cost of operating without one is — across slower delivery cycles, redundant spend, and governance risk that does not surface until it becomes a material issue.
Speak With Ascentt's Data Platform Team
Ascentt works with global enterprises to design and implement governed data science platforms that reduce infrastructure cost, accelerate delivery, and create the foundation for AI at scale.
About Ascentt
Ascentt delivers enterprise AI, machine learning, and cloud-native engineering solutions to global organisations. With more than 18 years of experience, Ascentt turns complex enterprise data into real business outcomes — spanning AI/ML and Data Science, Advanced Analytics, Cloud Operations, Data Engineering, Managed Services, and Salesforce Cloud. In the automotive sector, Ascentt has delivered measurable outcomes across demand forecasting, production intelligence, warranty cost reduction, and data platform modernisation, working with global OEMs and Tier-1 suppliers at enterprise scale.
