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+# Effective ML Deployment
+
+ML Deployment - the process of deploying an AI/ML model from PoC to Production
+
+# Key Offerings
+<img width="550" alt="image" src="https://github.com/hema-dc/ML-Deployment/assets/93590728/9ff383ea-12b0-43a2-88c7-98a1537093b9">
+
+## Startups
+1. Gather / Assess 
+
+   Gather
+      * The requirements, goals and objectives
+      * Understand the AI/ML use case(s)
+      * Understand the current infrastructure requirements
+      * Understand the deployment expectations (Budget, timescales, resources)
+
+   Assess
+      * The current AI/ML model
+      * The maturity of the code
+      * The current available infrastructure
+      * The current data pipeline (if any)
+      * The Data dependencies
+      * The size of the current dataset
+      * If current data is real or synthetic
+      * The data source for model in production
+      * Skills availability within the organisation
+3. Plan / Design
+      * Design the potential deployment pipeline
+      * Plan the deployment strategy
+      * Plan the metrics to be monitored
+4. Implement
+      * Deploy the model
+      * Review the deployment strategy
+5. Evaluate / Review
+      * Monitor the metrics of the model
+      * Observability - Model health, Data health and Service health
+
+## Large Organisations
+1. Gather / Assess 
+
+   Gather
+      * The requirements, goals and objectives
+      * Understand the AI/ML use case(s) and their connectivity to organisational strategy
+      * Understand the current infrastructure requirements
+      * Understand the deployment expectations (Budget, timescales, resources)
+
+   Assess
+      * The current AI/ML model
+      * Past AI/ML model (if any) already in production
+      * The maturity of the code
+      * The MLOps Maturity level
+      * The Scalability requirements
+      * The current available infrastructure
+      * The current data pipeline (if any)
+      * The Data dependencies
+      * The size of the current dataset
+      * If current data is real or synthetic
+      * The data source for model in production
+      * Skills availability within the organisation
+3. Plan / Design
+      * Design the potential deployment pipeline
+      * Plan the deployment strategy
+      * Plan the metrics to be monitored
+4. Execute
+      * Deploy the model
+      * Review the deployment strategy
+5. Evaluate / Review
+      * Monitor the metrics of the model
+      * Observability - Model health, Data health and Service health
+
+# Topics Covered
+1. Gathering Requirements
+2. Assessing the organisation's ML Readiness - [Maturity Assessment](https://github.com/hema-dc/ML-Deployment/blob/main/Offerings/Maturity%20Assessment.md)
+3. [Best Practices](https://github.com/hema-dc/ML-Deployment/blob/main/Offerings/Best%20Practices.md)
+4. Horizon Scan of available MLOps Tools
+5. ML Deployment
+6. [Deployment Strategies](https://github.com/hema-dc/ML-Deployment/blob/main/Offerings/Strategies.md)
+7. [MLOps](https://github.com/hema-dc/ML-Deployment/blob/main/Offerings/MLOps.md) 
+8. MLOps Tools
+9. [ML Monitoring and Observability](https://github.com/hema-dc/ML-Deployment/blob/main/Offerings/Observability.md)
+10. [MLOps Skills](https://github.com/hema-dc/ML-Deployment/blob/main/Offerings/MLOps%20Skills.md)