• Problem definition

Predict diamond prices based on features such as carat, cut, colour, clarity, and size

• Data

The dataset was downloaded from Kaggle https://www.kaggle.com/datasets/joebeachcapital/diamonds

• Evaluation

The Evaluation Metrics is based on the Root Mean Squre Error.

• Feature

The datset provides the follow feature which include; carat, cut, colour, clarity, x_size, y_size, and z_size

• Modelling

The Model used for prediction are Xgboost Regressor, Linear Regression, Lasso, Ridge, K-Neighbors Regressor,

Decision Tree, Random Forest Regressor, AdaBoost Regressor, and Catboost Regressor

Workflows

1. Create template.py
2. Update config.yaml
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the dvc.yaml
10. app.py

How to run?

STEPS:

Clone the repository

https://github.com/abideen-olawuwo/diamondprediction

STEP 01- Create a conda environment after opening the repository

conda create -n  python=3.8 -y

conda activate diamondprediction

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command

python app.py

Now,

open up you local host and port

MLflow

• Documentation

cmd

• mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI= https://dagshub.com/abideen-olawuwo/diamondprediction-MLflow-DVC.mlflow
MLFLOW_TRACKING_USERNAME=abideen-olawuwo
MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/abideen-olawuwo/diamondprediction-MLflow-DVC.mlflow

export MLFLOW_TRACKING_USERNAME=abideen-olawuwo

export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbce77

DVC cmd

1. dvc init
2. dvc repro
3. dvc dag

About MLflow & DVC

MLflow

• Its Production Grade
• Trace all of your expriements
• Logging & taging your model

DVC

• Its very lite weight for POC only
• lite weight expriements tracker
• It can perform Orchestration (Creating Pipelines)

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-1

AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com

ECR_REPOSITORY_NAME = simple-app