README update:

:

README.md

@@ -33,12 +33,12 @@ The tech stack used:
 The project uses:
 
 1. [Pipenv](https://docs.pipenv.org/)
-2. [docker](docker.com)
-3. [mlflow]()
-4. [grafana]()
-5. [prefect]()
+2. [docker](https://docker.com)
+3. [mlflow](https://mlflow.org)
+4. [grafana](https://grafana.com)
+5. [prefect](https://prefect.io)
 
-And th VM used for the project:
+And th VM used for the project (AWS EC2 instance):
 
 ![](/assets/aws_instance.png)
 
@@ -113,19 +113,18 @@ For experiment tracking and model registry we use `mlflow`
 We use prefect for orchestration in:
 ![](/assets/prefect_flows.png)
 
-1. Model training
+1. Model training `src/training_pipeline.py`
 ![](/assets/model_training_prefect.png)
-2. Model inference (Predict new data)
-![](/assets/score_churn_prefect.png)
-3. Model monitoring (Calculate drift, and model performance)
+2. Model inference (Predict new data) `src/batch_scoring_pipeline.py`
+![](/assets/score_churn_prefect.png))
+3. Model monitoring (Calculate drift, and model performance) `src/monitor_ml_churn_model.py`
 ![](/assets/monitoring_prefect.png)
 
 
 # 5. Model Deployment
 
 Deployment is done via `Makefile` + `Dockerfile`
 
-
 # 6. Model monitoring
 
 Machine learning monitoring with Grafana and Postgres involves using these two tools to track, visualize, and analyze the performance and behavior of machine learning models deployed in production. Let's break down how each component contributes to the monitoring process:
@@ -153,6 +152,6 @@ Grafana
 
 ```
 echo "Build dockerfile"
-docker build -t customer_churn_ml_pipeline .
+docker build -t customer_churn_ml_pipeline ./src/deployment
 docker run -v $(pwd):/app/ -it customer_churn_ml_pipeline
 ```