{"id":18368097,"url":"https://github.com/am-ankitgit/mlops_with_aws_deployment","last_synced_at":"2025-04-10T17:55:05.833Z","repository":{"id":252536765,"uuid":"840731110","full_name":"AM-Ankitgit/MLOPS_with_AWS_Deployment","owner":"AM-Ankitgit","description":"creating project from scratched to implement the MLOPs with Deployment on AWS","archived":false,"fork":false,"pushed_at":"2024-11-07T14:17:52.000Z","size":176,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T20:52:06.701Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"CSS","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AM-Ankitgit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-10T14:11:06.000Z","updated_at":"2024-11-07T14:17:56.000Z","dependencies_parsed_at":"2024-08-10T15:28:34.783Z","dependency_job_id":"97a0e71d-5126-4bf8-95e9-8027977f3198","html_url":"https://github.com/AM-Ankitgit/MLOPS_with_AWS_Deployment","commit_stats":null,"previous_names":["am-ankitgit/mlops_with_aws_deployment"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AM-Ankitgit%2FMLOPS_with_AWS_Deployment","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AM-Ankitgit%2FMLOPS_with_AWS_Deployment/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AM-Ankitgit%2FMLOPS_with_AWS_Deployment/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AM-Ankitgit%2FMLOPS_with_AWS_Deployment/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AM-Ankitgit","download_url":"https://codeload.github.com/AM-Ankitgit/MLOPS_with_AWS_Deployment/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248262438,"owners_count":21074308,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-05T23:24:35.610Z","updated_at":"2025-04-10T17:55:05.818Z","avatar_url":"https://github.com/AM-Ankitgit.png","language":"CSS","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Wine_Qauitiy_MLOPS_with_AWS_Deployment_With_CDCI Pipleine\n\nCreating project from scratched to implement the MLOPs with Deployment on AWS\n\n\n\n## Workflows\n\n1. Update config.yaml\n2. Update schema.yaml #schema of your data\n3. Update params.yaml\n4. Update the entity\n5. Update the configuration manager in src config\n6. Update the components\n7. Update the pipeline \n8. Update the main.py\n9. Update the app.py\n\n\n\n# How to run?\n### STEPS:\n\nClone the repository\n\n```bash\nhttps://github.com/AM-Ankitgit/MLOPS_with_AWS_Deployment.git\n```\n### STEP 01- Create a conda environment after opening the repository\n\n```bash\nconda create -n venv python=3.8 -y\n```\n\n```bash\nconda activate mlproj\n```\n\n\n### STEP 02- install the requirements\n```bash\npip install -r requirements.txt\n```\n\n\n```bash\n# Finally run the following command\npython app.py\n```\n\nNow,\n```bash\nopen up you local host and port\n```\n\n\n## MLflow\n\n[Documentation](https://mlflow.org/docs/latest/index.html)\n\n\n##### cmd\n- mlflow ui\n\n\n### dagshub\nmlflow tracking url  (this url is created by dagshub)\n\nfor windows user set url and you can see the dashboard of mlflow\n\nset MLFLOW_TRACKING_URI=https://dagshub.com/AM-Ankitgit/MLOPS_with_AWS_Deployment.mlflow\n\nset MLFLOW_TRACKING_USERNAME=AM-Ankitgit\n\nset MLFLOW_TRACKING_PASSWORD=9a0cfdb8c9f9890d8d9e80455ae5918fcb9f4cb6\n\n```\nimport dagshub\ndagshub.init(repo_owner='AM-Ankitgit', repo_name='MLOPS_with_AWS_Deployment', mlflow=True)\n\n# example script :\n\nimport mlflow\nwith mlflow.start_run():\n  mlflow.log_param('parameter name', 'value')\n  mlflow.log_metric('metric name', 1)\n\n\n```\n\nRun this to export as env variables:\n\n```bash\n\nexport MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/End-to-end-Machine-Learning-Project-with-MLflow.mlflow\n\nexport MLFLOW_TRACKING_USERNAME=entbappy \n\nexport MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0\n\n```\n\n\n\n# AWS-CICD-Deployment-with-Github-Actions\n\n## 1. Login to AWS console.\n\n## 2. Create IAM user for deployment\n\n\t#with specific access\n\n\t1. EC2 access : It is virtual machine\n\n\t2. ECR: Elastic Container registry to save your docker image in aws\n\n\n\t#Description: About the deployment\n\n\t1. Build docker image of the source code\n\n\t2. Push your docker image to ECR\n\n\t3. Launch Your EC2 \n\n\t4. Pull Your image from ECR in EC2\n\n\t5. Lauch your docker image in EC2\n\n\t# Name of Policy:\n\n\t1. AmazonEC2ContainerRegistryFullAccess\n\n\t2. AmazonEC2FullAccess\n\n\t\n## 3. Create ECR repo to store/save docker image\n    - Save the URI: 322848076327.dkr.ecr.us-east-1.amazonaws.com/mlproject\n\n\t\n## 4. Create EC2 machine (Ubuntu) \n\n## 5. Open EC2 and Install docker in EC2 Machine:\n\t\n\topen the ec2 terminal\n\t#optinal\n\n\tsudo apt-get update -y\n\n\tsudo apt-get upgrade\n\t\n\t#required\n\n\tcurl -fsSL https://get.docker.com -o get-docker.sh\n\n\tsudo sh get-docker.sh\n\n\tsudo usermod -aG docker ubuntu\n\n\tnewgrp docker\n\n    # check your docker is installed\n    docker --version\n\t\n# 6. Configure EC2 as self-hosted runner:\n    setting\u003eactions\u003erunner\u003enew self hosted runner\u003e choose os\u003e then run command one by one\n    ![alt text](image.png)\n\n\n# 7. Setup github secrets:\n\n    AWS_ACCESS_KEY_ID=\n\n    AWS_SECRET_ACCESS_KEY=\n\n    AWS_REGION = us-east-1\n\n    AWS_ECR_LOGIN_URI = demo\u003e\u003e  566373416292.dkr.ecr.ap-south-1.amazonaws.com\n\n    ECR_REPOSITORY_NAME = mlproj\n\tECR_REPOSITORY_NAME diamond\n\n\n\n\n## About MLflow \nMLflow\n\n - Its Production Grade\n - Trace all of your expriements\n - Logging \u0026 tagging your model\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fam-ankitgit%2Fmlops_with_aws_deployment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fam-ankitgit%2Fmlops_with_aws_deployment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fam-ankitgit%2Fmlops_with_aws_deployment/lists"}