Implementing MLOps Framework on Google Cloud to Streamline Data

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: 

1. Conducted comprehensive sessions with the client to understand the use case, data schema, columns, and tables. 
2. Prepared a detailed assessment of the current state and proposed a high-level solution architecture for implementation. 

Pilot Implementation Phase: 

1. Focused on architecting, modeling, inference, and model serving.
2. Demonstrated model monitoring and CI/CD frameworks.
3. Custom-developed an ML Ops pipeline tailored for the client’s Churn Prediction use case.

RESULTS

1. Enhanced capability to handle complex data and ML processes efficiently. 
2. Overcame memory constraints, leading to better payload management. 
3. Enabled the client to modernize customer interactions & feedback mechanisms more effectively. 
4. The framework provided predictive insights, helping in proactive customer retention strategies.