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55b59263f4
updated main.yaml
2 months ago
8b08e839d3
Implemented data ingestion pipeline
2 months ago
a4480fe6b3
Implemented model evaluation and added mlflow
2 months ago
src
84af9c31c7
added simple ui and implemented flask app
2 months ago
84af9c31c7
added simple ui and implemented flask app
2 months ago
84af9c31c7
added simple ui and implemented flask app
2 months ago
84af9c31c7
added simple ui and implemented flask app
2 months ago
c23f8c91fb
updated dockerfile
2 months ago
e4287b62bf
Initial commit
2 months ago
1cba712f20
updated readme
2 months ago
84af9c31c7
added simple ui and implemented flask app
2 months ago
84af9c31c7
added simple ui and implemented flask app
2 months ago
a4480fe6b3
Implemented model evaluation and added mlflow
2 months ago
0aee1f180d
added requirements
2 months ago
c5c59c1401
added data validation pipeline
2 months ago
0aee1f180d
added requirements
2 months ago
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README.md

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rk_the_bartender_II

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

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

How to run?

STEPS:

Clone the repository

https://github.com/rodinKaradeniz/rk_the_bartender_II

STEP 01- Create a conda environment after opening the repository

conda create -n rk_the_bartender_mlops python=3.8 -y
conda activate rk_the_bartender_mlops

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/rodinKaradeniz/rk_the_bartender_II.mlflow MLFLOW_TRACKING_USERNAME=rodinKaradeniz MLFLOW_TRACKING_PASSWORD=853a0ce7db49d48ed5c58be6eadcdb9e95d29081 python script.py

Run this to export as env variables:


export MLFLOW_TRACKING_URI=https://dagshub.com/rodinKaradeniz/rk_the_bartender_II.mlflow

export MLFLOW_TRACKING_USERNAME=rodinKaradeniz

export MLFLOW_TRACKING_PASSWORD=853a0ce7db49d48ed5c58be6eadcdb9e95d29081

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: 381492003052.dkr.ecr.us-east-2.amazonaws.com/rk_the_bartender_mlops

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optional

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-2

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
Tip!

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