[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9624":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":31,"readmeContent":32,"aiSummary":33,"trendingCount":16,"starSnapshotCount":16,"syncStatus":34,"lastSyncTime":35,"discoverSource":36},9624,"mlops-zoomcamp","DataTalksClub\u002Fmlops-zoomcamp","DataTalksClub","Free MLOps course from DataTalks.Club","",null,"Jupyter Notebook",14772,2958,215,3,0,4,54,174,33,45,false,"main",true,[26,27,28,29,30],"machine-learning","mlops","model-deployment","model-monitoring","workflow-orchestration","2026-06-12 02:02:10","\u003Cp align=\"center\">\n  \u003Cimg width=\"80%\" src=\"images\u002Fbanner-2025.jpg\" alt=\"MLOps Zoomcamp\">\n\u003C\u002Fp>\n\n\u003Ch1 align=\"center\">\n    \u003Cstrong>MLOps Zoomcamp: A Free 9-Week Course on Productionizing ML Services\u003C\u002Fstrong>\n\u003C\u002Fh1>\n\n\u003Cp align=\"center\">\nMLOps (machine learning operations) is a must-know skill for many data professionals. Master the fundamentals of MLOps, from training and experimentation to deployment and monitoring.\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fairtable.com\u002FshrCb8y6eTbPKwSTL\">\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F875246\u002F185755203-17945fd1-6b64-46f2-8377-1011dcb1a444.png\" height=\"50\" \u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fdatatalks.club\u002Fslack.html\">Join Slack\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fapp.slack.com\u002Fclient\u002FT01ATQK62F8\u002FC01FABYF2RG\">#course-mlops-zoomcamp Channel\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Ft.me\u002Fdtc_courses\">Telegram Announcements\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL3MmuxUbc_hIUISrluw_A7wDSmfOhErJK\">Course Playlist\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fdatatalks.club\u002Ffaq\u002Fmlops-zoomcamp.html\">FAQ\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fctt.ac\u002FfH67W\">Tweet about the Course\u003C\u002Fa>\n\u003C\u002Fp>\n\n## How to Take MLOps Zoomcamp\n\n### 2026 Cohort\n\n* We don't plan to offer the course in 2026\n* You can still take it self-paced\n* [**Register Here**](https:\u002F\u002Fairtable.com\u002FshrCb8y6eTbPKwSTL) if you want to receive updates if we decide to run the course\n\n### Self-Paced Learning\nAll course materials are freely available for independent study. Follow these steps:\n1. Watch the course videos.\n2. Join the [Slack community](https:\u002F\u002Fdatatalks.club\u002Fslack.html).\n3. Refer to the [FAQ document](https:\u002F\u002Fdatatalks.club\u002Ffaq\u002Fmlops-zoomcamp.html) for guidance.\n\n## Syllabus\nThe course consists of structured modules, hands-on workshops, and a final project to reinforce your learning. Each module introduces core MLOps concepts and tools.\n\n### Prerequisites\nTo get the most out of this course, you should have prior experience with:\n- Python\n- Docker\n- Command line basics\n- Machine learning (e.g., through [ML Zoomcamp](https:\u002F\u002Fgithub.com\u002Falexeygrigorev\u002Fmlbookcamp-code\u002Ftree\u002Fmaster\u002Fcourse-zoomcamp))\n- 1+ year of programming experience\n\n## Modules\n\n### [Module 1: Introduction](01-intro)\n- What is MLOps?\n- MLOps maturity model\n- NY Taxi dataset (our running example)\n- Why MLOps is essential\n- Course structure & environment setup\n- Homework\n\n### [Module 2: Experiment Tracking & Model Management](02-experiment-tracking)\n- Introduction to experiment tracking\n- MLflow basics\n- Model saving and loading\n- Model registry\n- Hands-on MLflow exercises\n- Homework\n\n### [Module 3: Orchestration & ML Pipelines](03-orchestration)\n\n- Workflow orchestration\n- Homework\n\n### [Module 4: Model Deployment](04-deployment)\n- Deployment strategies: online (web, streaming) vs. offline (batch)\n- Deploying with Flask (web service)\n- Streaming deployment with AWS Kinesis & Lambda\n- Batch scoring for offline processing\n- Homework\n\n### [Module 5: Model Monitoring](05-monitoring)\n- Monitoring ML-based services\n- Web service monitoring with Prometheus, Evidently, and Grafana\n- Batch job monitoring with Prefect, MongoDB, and Evidently\n- Homework\n\n### [Module 6: Best Practices](06-best-practices)\n- Unit and integration testing\n- Linting, formatting, and pre-commit hooks\n- CI\u002FCD with GitHub Actions\n- Infrastructure as Code (Terraform)\n- Homework\n\n### [Final Project](07-project\u002F)\n- End-to-end project integrating all course concepts\n\n## Community & Support\n\n### Getting Help on Slack\n\nJoin the [`#course-mlops-zoomcamp`](https:\u002F\u002Fapp.slack.com\u002Fclient\u002FT01ATQK62F8\u002FC02R98X7DS9) channel on [DataTalks.Club Slack](https:\u002F\u002Fdatatalks.club\u002Fslack.html) for discussions, troubleshooting, and networking.\n\nTo keep discussions organized:\n- Follow [our guidelines](asking-questions.md) when posting questions.\n- Review the [community guidelines](https:\u002F\u002Fdatatalks.club\u002Fslack\u002Fguidelines.html).\n\n## Instructors\n\n- [Cristian Martinez](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fcristian-javier-martinez-09bb7031\u002F)\n- [Alexey Grigorev](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fagrigorev\u002F)\n- [Emeli Dral](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Femelidral\u002F)\n\n\n## Sponsors & Supporters\n\nInterested in supporting our community? Reach out to [alexey@datatalks.club](mailto:alexey@datatalks.club).\n\n## About DataTalks.Club\n\n\u003Cp align=\"center\">\n  \u003Cimg width=\"40%\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F1243a44a-84c8-458d-9439-aaf6f3a32d89\" alt=\"DataTalks.Club\">\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fdatatalks.club\u002F\">DataTalks.Club\u003C\u002Fa> is a global online community of data enthusiasts. It's a place to discuss data, learn, share knowledge, ask and answer questions, and support each other.\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fdatatalks.club\u002F\">Website\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fdatatalks.club\u002Fslack.html\">Join Slack Community\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fus19.campaign-archive.com\u002Fhome\u002F?u=0d7822ab98152f5afc118c176&id=97178021aa\">Newsletter\u003C\u002Fa> •\n\u003Ca href=\"http:\u002F\u002Flu.ma\u002Fdtc-events\">Upcoming Events\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002F@DataTalksClub\u002Ffeatured\">YouTube\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FDataTalksClub\">GitHub\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fdatatalks-club\u002F\">LinkedIn\u003C\u002Fa> •\n\u003Ca href=\"https:\u002F\u002Ftwitter.com\u002FDataTalksClub\">Twitter\u003C\u002Fa>\n\u003C\u002Fp>\n\nAll the activity at DataTalks.Club mainly happens on [Slack](https:\u002F\u002Fdatatalks.club\u002Fslack.html). We post updates there and discuss different aspects of data, career questions, and more.\n\nAt DataTalksClub, we organize online events, community activities, and free courses. You can learn more about what we do at [DataTalksClub Community Navigation](https:\u002F\u002Fwww.notion.so\u002FDataTalksClub-Community-Navigation-bf070ad27ba44bf6bbc9222082f0e5a8?pvs=21).\n","MLOps Zoomcamp 是一个由 DataTalks.Club 提供的免费9周课程，旨在教授如何将机器学习服务投入生产。该项目通过Jupyter Notebook形式提供了从模型训练、实验跟踪到部署和监控等一系列核心MLOps概念和技术的学习材料。它覆盖了使用MLflow进行实验跟踪与模型管理、利用Flask等工具部署在线及离线服务以及工作流编排等内容。适合具有一定Python编程基础、了解Docker和命令行操作，并对机器学习有一定认识的数据专业人员自学或作为团队培训资源使用。",2,"2026-06-11 03:23:49","top_topic"]