MLOps Framework Solution to ML Business Problem

Prior Situation / Scenario:

• Business Problem: increasing number of subscribers with involuntary churn.​
• Over 50% of machine learning projects are never put into production due to labor intensive and inconsistent workflows.
• Manual ML model improvement in response to performance deterioration.
• Lack of scalability and high impact of staff turnover due to key-person dependency.

Client Challenges:

• Estimate daily propensity to involuntary churn (score/prediction) for each customer to improve Retention & Collection campaigns.
• Manage the lifecycle of the ML solution.
• Standardize operationalization.
• Enable or disable functionality remotely without deploying code​.
• Automatic and self-healing ML model (retraining).
• Misuse of talent: high amount of manual task and low business impactful tasks.

Strata Solution/ Key Enablers:

• High-performance leading-edge ML algorithm portfolio
• ML Solution as a Product
  • Deployed in customer cloud account​
  • ML Model Development and Operationalization Manual ​
• Serverless and Event Driven System​
• Agile development: MVP + incremental functionalities

Outcome:

MLOps Framework:

• High scalability and high availability​ architecture
• Delivered as a product with capabilities to: Data Process, Prediction, Monitoring, Automatic Retraining (model optimization).
• Time to market: one click deployment into production.
• Agile improvements: granular releases into production (MVP)
• Standardized, easy control, operation and maintenance​ of ML Solution​