{"id":22154125,"url":"https://github.com/nathadriele/mlops-zoomcamp","last_synced_at":"2025-04-10T16:32:38.268Z","repository":{"id":240429801,"uuid":"802603183","full_name":"nathadriele/mlops-zoomcamp","owner":"nathadriele","description":"The Zoomcamp MLOps Course covers tools like MLflow, Mage, Flask, Prometheus, Evidently, Grafana, Prefect, Terraform, and GitHub Actions. It emphasizes experiment tracking, model deployment, monitoring, CI/CD, and orchestration, culminating in an end-to-end project integrating best practices in MLOps.","archived":false,"fork":false,"pushed_at":"2025-02-18T15:38:08.000Z","size":92668,"stargazers_count":10,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-24T14:16:44.598Z","etag":null,"topics":["aws","batch-processing","flask","grafana","mage-ai","mlflow","mlops","model","mongodb","monitoring","orchestration","prefect","streaming"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nathadriele.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-05-18T18:55:35.000Z","updated_at":"2025-02-18T15:38:13.000Z","dependencies_parsed_at":"2024-05-18T20:27:42.943Z","dependency_job_id":"73d45811-09b8-4752-ad5a-c8af0f75d39f","html_url":"https://github.com/nathadriele/mlops-zoomcamp","commit_stats":null,"previous_names":["nathadriele/mlops-zoomcamp"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nathadriele%2Fmlops-zoomcamp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nathadriele%2Fmlops-zoomcamp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nathadriele%2Fmlops-zoomcamp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nathadriele%2Fmlops-zoomcamp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nathadriele","download_url":"https://codeload.github.com/nathadriele/mlops-zoomcamp/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248252696,"owners_count":21072701,"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":["aws","batch-processing","flask","grafana","mage-ai","mlflow","mlops","model","mongodb","monitoring","orchestration","prefect","streaming"],"created_at":"2024-12-02T01:41:03.584Z","updated_at":"2025-04-10T16:32:33.240Z","avatar_url":"https://github.com/nathadriele.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Zoomcamp MLOps Course\n\n[More details](https://github.com/DataTalksClub/mlops-zoomcamp)\n\n🟢 **Final project developed**\n\nhttps://github.com/nathadriele/vercel-app-mlops-zoomcamp-project-paris-price-house\n\n![image](https://github.com/user-attachments/assets/0509c5c9-e854-4adc-b33f-b0fce23892c6)\n\n## 1. Introduction\n- 1.1 What is MLOps\n- 1.2 MLOps maturity model\n- 1.3 Running example: NY Taxi trips dataset\n- 1.4 Why do we need MLOps\n- 1.5 Environment preparation\n\n## 2. Experiment tracking and model management\n- 2.1 Experiment tracking intro\n- 2.2 Getting started with MLflow\n- 2.3 Experiment tracking with MLflow\n- 2.4 Saving and loading models with MLflow\n- 2.5 Model registry\n- 2.6 MLflow in practice\n\n## 3. Orchestration and ML Pipelines\n- 3.1 Workflow orchestration\n- 3.2 Mage\n\n## 4. Model Deployment\n- 4.1 Three ways of model deployment: Online (web and streaming) and offline (batch)\n- 4.2 Web service: model deployment with Flask\n- 4.3 Streaming: consuming events with AWS Kinesis and Lambda\n- 4.4 Batch: scoring data offline\n\n## 5. Model Monitoring\n- 5.1 Monitoring ML-based services\n- 5.2 Monitoring web services with Prometheus, Evidently, and Grafana\n- 5.3 Monitoring batch jobs with Prefect, MongoDB, and Evidently\n\n## 6. Best Practices\n- 6.1 Testing: unit, integration\n- 6.2 Python: linting and formatting\n- 6.3 Pre-commit hooks and makefiles\n- 6.4 CI/CD (GitHub Actions)\n- 6.5 Infrastructure as code (Terraform)\n\n## 7. Project\n- 7.1 End-to-end project with all the things above\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnathadriele%2Fmlops-zoomcamp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnathadriele%2Fmlops-zoomcamp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnathadriele%2Fmlops-zoomcamp/lists"}