Model Deployment Architecture

 My implementation experience and lessons :)

Product Implementation (2012-2014)

• Integrated in product
• Jobs scheduled for midnight
• Workflow to monitor variations
• Forecast updated every day for store
• Everything custom-coded formula embedded
• Weighted moving average 
• Step up / Step down moving average approach

Batched State of Art (2021)

Recommendations AWS

• ETL / Glue jobs to get featured
• Full pull/delta pull scripts
• Feature engineering scripts
• Custom segmentation scripts
• Batch jobs to run models
• Large-scale recommendations generation
• Infra kubeflow setup 
• Leverage existing Kubeflow monitoring setup

Forecasting State of Art (2021)

Kubeflow + AWS

• ETL / Glue jobs to get features
• Full pull/delta pull scripts
• Feature engineering scripts
• Custom segmentation scripts
• Batch jobs to run models
• Kubeflow pipelines for the forecast
• Results persist in Redshift DB
• Infra kubeflow setup 
• Leverage existing kubeflow monitoring setup

Realtime State of Art (2022)

Real-time streaming / Vision Solution

• AWS Lamdbda-based approach
• Vision + Docker + AWS Lambda
• Request monitoring / logging

Keep Exploring!!!