End-to-end-Mlops

End to end Mlops Automation

End-to-end-Machine-Learning-Project-with-MLflow

Workflows

Update config.yaml
Update schema.yaml
Update params.yaml
Update the entity
Update the configuration manager in src config
Update the components
Update the pipeline
Update the main.py
Update the app.py

How to run?

STEPS:

Clone the repository

https://github.com/xxxxx/End-to-end-Machine-Learning-Project-with-MLflow

STEP 01- Create a conda environment after opening the repository

conda create -n mlproj python=3.8 -y

conda activate mlproj

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

MLFLOW_TRACKING_URI=https://dagshub.com/xxxxx/End-to-end-Machine-Learning-Project-with-MLflow.mlflow
MLFLOW_TRACKING_USERNAME=xxxxx
MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/xxxxx/End-to-end-Machine-Learning-Project-with-MLflow.mlflow

export MLFLOW_TRACKING_USERNAME=xxxxx 

export MLFLOW_TRACKING_PASSWORD=xxxxxx

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.ap-south-1.amazonaws.com/mlproj

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

About MLflow

MLflow

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

srinivasdasari2023 / end-to-end-mlops Goto Github PK

end-to-end-mlops's Introduction

End-to-end-Mlops

End-to-end-Machine-Learning-Project-with-MLflow

Workflows

How to run?

STEPS:

STEP 01- Create a conda environment after opening the repository

STEP 02- install the requirements

MLflow

cmd

dagshub

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

3. Create ECR repo to store/save docker image

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

6. Configure EC2 as self-hosted runner:

7. Setup github secrets:

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