End-to-End-MLOps-Project

Workflows

1. Update config.yaml
2. Update schema.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 app.py

Pipelines

1. Data Ingestion
2. Data Validation
3. Data Transformation
4. Model Trainer
5. Model Evaluation

STEPS

Clone the repository

git clone https://github.com/CodeWithCharan/End-to-End-MLOps-Project.git

STEP 01: Create a conda environment after opening the repository

conda create -n mlopsenv python=3.8 -y

conda activate mlopsenv

STEP 02: install the requirements

pip install -r requirements.txt

STEP 03: run app.py

python app.py

STEP 04: After running the app.py, it will be available at:

• http://127.0.0.1:8080
• http://localhost:8080

MLFlow

Documentation

CMD

mlflow ui

DagsHub

Documentation

Tracking URI:

• MLFLOW_TRACKING_URI = https://dagshub.com/CodeWithCharan/End-to-End-MLOps-Project.mlflow

• MLFLOW_TRACKING_USERNAME = CodeWithCharan

• MLFLOW_TRACKING_PASSWORD = YourAccessToken

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/CodeWithCharan/End-to-End-MLOps-Project.mlflow

export MLFLOW_TRACKING_USERNAME=CodeWithCharan

export MLFLOW_TRACKING_PASSWORD=YourAccessToken

AWS-CICD-Deployment-with-Github-Actions

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

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

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

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 = ap-south-1

AWS_ECR_LOGIN_URI =

ECR_REPOSITORY_NAME = mlproj

About MLflow

MLflow

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