Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
9d590d09f6
prediction pipeline and docker created
1 month ago
dd8dc5682e
model evaluation and mlflow added
1 month ago
bc77ad4116
git link added
3 weeks ago
src
8d9c351e57
changes happen
3 weeks ago
9d590d09f6
prediction pipeline and docker created
1 month ago
ac79366aeb
index.html changes
3 weeks ago
fdf947f220
data injection added
1 month ago
9a3630cc69
docker changes
1 month ago
acdfd5c8fd
Initial commit
2 months ago
8d9c351e57
changes happen
3 weeks ago
b310694547
chnages in app.py
1 month ago
dd8dc5682e
model evaluation and mlflow added
1 month ago
9d590d09f6
prediction pipeline and docker created
1 month ago
bc77ad4116
git link added
3 weeks ago
dd8dc5682e
model evaluation and mlflow added
1 month ago
b4f3fbd752
requirements added
1 month ago
0f01354d65
data validation added
1 month ago
b4f3fbd752
requirements added
1 month ago
a55ab6d34e
folder structure added
1 month ago
a55ab6d34e
folder structure added
1 month ago
Storage Buckets

README.md

You have to be logged in to leave a comment. Sign In

end-to-end-MLOPS-project-by-atishay

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/atishayrokadia/end-to-end-MLOPS-project-by-atishay

STEP 01- Create a conda environment after opening the repository

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

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/atishayrokadia2402/end-to-end-MLOPS-project-by-atishay.mlflow MLFLOW_TRACKING_USERNAME=atishayrokadia2402 MLFLOW_TRACKING_PASSWORD=b384d2c498ba8f62f4a9befd1f8819ac9ee3972f python script.py

Run this to export as env variables:


export MLFLOW_TRACKING_URI=https://dagshub.com/atishayrokadia2402/end-to-end-MLOPS-project-by-atishay.mlflow

export MLFLOW_TRACKING_USERNAME=atishayrokadia2402 

export MLFLOW_TRACKING_PASSWORD=b384d2c498ba8f62f4a9befd1f8819ac9ee3972f

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: 260544533631.dkr.ecr.us-east-1.amazonaws.com/mlopsproj
                260544533631.dkr.ecr.us-east-1.amazonaws.com/mlopsproj

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>>  260544533631.dkr.ecr.us-east-1.amazonaws.com


ECR_REPOSITORY_NAME = mlopsproj

About MLflow

MLflow

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

Press p or to see the previous file or, n or to see the next file

About

No description

Collaborators 1

Comments

Loading...