Machine Learning Operations (MLOps) implementation strategy to scale the DSPs’ ML use cases

October 12, 2021

Most of the Digital Service Providers (DSPs) struggle to scale the Machine Learning (ML) pilots into production, which limits their ability to realize the potential business value of the ML use cases. According to Gartner, “Launching pilots is deceptively easy but deploying them into production is notoriously challenging”. As DSPs develop numerous ML pilots, they face various challenges in operationalizing the ML models at scale due to:

  • Lack of a standard change management process to address the change request in ML pipeline
  • Inefficient ways of re-training and deploying the ML models to accommodate the data drift
  • Lack of in-depth visibility of the model’s performance

To overcome these challenges and scale the AI/ML initiatives, DSPs need to implement the MLOps approach that automates and monitors the entire machine learning lifecycle. Leveraging this MLOps approach enables cost savings, improves the baseline accuracy, and accelerates time-to-production of AI/ML models.

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The successful implementation of the MLOps approach requires the right set of enablers such as de-coupled architecture, standardized change management process, automated retraining and deployment of ML models, and continuous monitoring.

Download this insight to know more about:

  • How DSPs can implement MLOps approach to scale the ML use cases
  • The four key levers and best practices for successful implementation of MLOps approach
  • Business benefits achieved by a leading DSP in the Americas after successful implementation of MLOps approach – 70% reduction in model re-training and deployment time, 50% OpEx savings, reduction in model prediction time, and 75 to 85% consistent improvement in the baseline accuracy of the ML use cases

Authors:

  • Skanda Gurunathan
  • Dinesh Singh GC
  • Prashantkumar Maloo
  • Priyankaa A

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