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Implementing MLOps Framework on Google Cloud to Streamline Data
Jan 2020–Aug 2021
GOALS
To evaluate and enhance the client’s current Data and ML landscape by implementing an MLOps framework on Google Cloud (Vertex AI), focusing on modernizing customer interactions and improving online reputations.
CHALLENGES
Diverse Programming Languages: Varied languages used at different pipeline stages created complexity.
Memory Constraints: Limited memory capacity for online inference payloads impacted performance.
OUR APPROACH
Trigma's approach involved a two-phased strategy:
Discovery Phase:
- Conducted comprehensive sessions with the client to understand the use case, data schema, columns, and tables.
- Prepared a detailed assessment of the current state and proposed a high-level solution architecture for implementation.
Pilot Implementation Phase:
- Focused on architecting, modeling, inference, and model serving.
- Demonstrated model monitoring and CI/CD frameworks.
- Custom-developed an ML Ops pipeline tailored for the client's Churn Prediction use case.
RESULTS
- Enhanced capability to handle complex data and ML processes efficiently.
- Overcame memory constraints, leading to better payload management.
- Enabled the client to modernize customer interactions & feedback mechanisms more effectively.
- The framework provided predictive insights, helping in proactive customer retention strategies.
CONCLUSION
Trigma’s tailored MLOps framework on Google Cloud (Vertex AI) significantly improved the client's data and ML landscape. This strategic enhancement not only streamlined their operational processes but also provided a robust foundation for modernizing customer interactions and feedback systems.
