Problem statement
Our client, a leading automotive OEM, lacked a comprehensive MLOps framework and code versioning, which impacted the business’s performance and reliability on ML models. Their processes relied on manual intervention for different model iterations, causing significant delays in use-case implementation, and more.
MathCo identified this significant gap in their process and proposed a central MLOps framework that would automate all manual processes, streamline project timelines, and ensure high-quality models that are easily accessible and scalable.
Impact
- Automated experimental tracking
- Enabled reusability of model components
- Established protocols for model governance
- Activated conditional training to optimize resource distribution
- Deployed decoupled pipelines for effortless debugging and provisioning of specific code segments to enhance overall efficiency
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