Developing MLOps Framework to Optimize ML Models

Solution MLOps Framework Implementation on CDP
Solution MLOps Framework Implementation on CDP
Industry Automotive
Region US
Technology Customer Data Platform
Context
AI and ML play a pivotal role in the automotive industry to increase manufacturing efficiency through actionable insights. With ML models on the rise to tackle any business challenge, streamlined processes are necessary to ensure they remain adequately trained and updated. This daunting and time-consuming task can be managed with a well-defined MLOps framework that allows organizations to automate the retraining of ML models and optimize their end-to-end lifecycle. Businesses can also bridge the gaps between development and deployment and reduce time-to-market and operational costs.  
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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