Forecasting Setup MVP to Large Scale Deployment

Phase I (Initial use cases)

• DVC - Data Versioning and Control
• Databases - Sales / Historical data
• External Marketing Campaign data 
• Feature Engineering - Database / Feature Store
• Model Building - ML / DL Algos
• Model Experiment Tracking - MLflow
• Drift / Monitoring - Evidently AI
• Model Deployment - API / Serverless function
• Actuals Tracking Loading - Database
• Model Scheduling / ETL Scheduling - Jobs based on AWS / GCP / Custom scheduler cron jobs
• I prefer dockerizing it and deploy it in GCP APP engine like a Nocode / Low code approach for the first few models
• Custom Reporting for trends/patterns making it more accessible/relatable to business
• Reporting for Past - Present - Future. Making predictions relatable
• Explainable AI to compare predictions vs actuals to interpret cause - -reason in a more Non-ML Approach

Phase II (Serving large models > 30)

• Kubernetes based Platform
• Once the platform is deployed you can leverage out of box images / notebook / pipeline / monitoring options available
• Kubeflow Pipelines

Kubernetes as a Service: GKE vs. AKS vs. EKS

Build a Pipeline

Keep Exploring!!!