[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-mlops-zoomcamp-path-to-production-ml-en":3,"article-related-mlops-zoomcamp-path-to-production-ml-en":33,"series-industry-8c10e73a-b4e7-444b-9a70-421823b16755":86},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":25,"views":29,"created_at":30,"published_at":31,"topic_cluster_id":32},"8c10e73a-b4e7-444b-9a70-421823b16755","mlops-zoomcamp-path-to-production-ml-en","MLOps Zoomcamp maps the path to production ML","\u003Cp data-speakable=\"summary\">This free course shows how to build, deploy, and monitor ML systems end to end.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"\u002Ftag\u002Fmlops\">MLOps\u003C\u002Fa> Zoomcamp is a free 9-week course with 14.8k \u003Ca href=\"\u002Ftag\u002Fgithub\">GitHub\u003C\u002Fa> stars that turns \u003Ca href=\"\u002Ftag\u002Fmachine-learning\">machine learning\u003C\u002Fa> basics into production practice. If you want a guided path from experiments to monitoring, these seven parts show what each module covers and who it helps most.\u003C\u002Fp>\n\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Item\u003C\u002Fth>\u003Cth>Focus\u003C\u002Fth>\u003Cth>Notable detail\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Experiment tracking\u003C\u002Ftd>\u003Ctd>Model management\u003C\u002Ftd>\u003Ctd>MLflow basics, registry, saving and loading\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Orchestration\u003C\u002Ftd>\u003Ctd>ML pipelines\u003C\u002Ftd>\u003Ctd>Workflow automation\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Deployment\u003C\u002Ftd>\u003Ctd>Serving models\u003C\u002Ftd>\u003Ctd>Online, streaming, and batch options\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Monitoring\u003C\u002Ftd>\u003Ctd>Service health\u003C\u002Ftd>\u003Ctd>Prometheus, Evidently, Grafana, Prefect, MongoDB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Best practices\u003C\u002Ftd>\u003Ctd>Engineering hygiene\u003C\u002Ftd>\u003Ctd>Testing, linting, pre-commit, CI\u002FCD, Terraform\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2>1. Introduction and setup\u003C\u002Fh2>\n\u003Cp>The opening module explains what MLOps is, why it matters, and how the course is organized. It also uses the NY Taxi dataset as a running example, which makes the lessons concrete instead of abstract.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781542983207-pzyb.png\" alt=\"MLOps Zoomcamp maps the path to production ML\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\n\u003Cp>This is the right starting point if you know basic machine learning but have not yet connected it to production workflows. The course asks for Python, \u003Ca href=\"\u002Ftag\u002Fdocker\">Docker\u003C\u002Fa>, command line basics, and about a year of programming experience, so it assumes some comfort with tools, not just notebooks.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>Core topics: MLOps maturity model, course structure, environment setup\u003C\u002Fli>\n  \u003Cli>Audience: data scientists, ML engineers, software engineers\u003C\u002Fli>\n  \u003Cli>Format: pre-recorded lectures with homework\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>2. Experiment tracking and model management\u003C\u002Fh2>\n\u003Cp>This module covers the first pain point many teams hit: remembering which run produced which model. It introduces experiment tracking, MLflow, model saving and loading, and the model registry, so you can compare runs and keep models organized.\u003C\u002Fp>\n\u003Cp>If you have ever lost track of hyperparameters, metrics, or model versions, this section gives a practical fix. It is especially useful for anyone moving from one-off training scripts to a repeatable workflow.\u003C\u002Fp>\n\u003Ccode>Topics: experiment tracking, MLflow basics, model registry, hands-on exercises, homework\u003C\u002Fcode>\n\u003Ch2>3. Orchestration and ML pipelines\u003C\u002Fh2>\n\u003Cp>Once experiments are under control, the course moves to orchestration. This module focuses on workflow orchestration and ML pipelines, which is the step that turns scattered scripts into a coordinated process.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781542981111-tr92.png\" alt=\"MLOps Zoomcamp maps the path to production ML\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\n\u003Cp>That matters when training, validation, and data preparation need to run in a reliable order. For teams that want fewer manual steps and less fragile glue code, this is one of the most practical sections in the syllabus.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>Goal: automate multi-step ML workflows\u003C\u002Fli>\n  \u003Cli>Outcome: reusable pipeline structure\u003C\u002Fli>\n  \u003Cli>Homework: build an orchestration exercise\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>4. Deployment strategies\u003C\u002Fh2>\n\u003Cp>The deployment module compares online, streaming, and offline serving, then shows how to ship models with Flask, AWS Kinesis, Lambda, and batch scoring. That mix is useful because not every model needs the same serving pattern.\u003C\u002Fp>\n\u003Cp>Readers who mainly know training will get a clearer picture of how models actually reach users and systems. The module helps you choose between a web service, a streaming setup, or batch processing based on the job at hand.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>Online deployment: web service or streaming\u003C\u002Fli>\n  \u003Cli>Offline deployment: batch scoring\u003C\u002Fli>\n  \u003Cli>Tools mentioned: Flask, AWS Kinesis, Lambda\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>5. Monitoring and service health\u003C\u002Fh2>\n\u003Cp>Monitoring is where production ML gets real, and this course treats it as a full module rather than an afterthought. It covers service monitoring with Prometheus, Evidently, and Grafana, plus batch monitoring with Prefect, MongoDB, and Evidently.\u003C\u002Fp>\n\u003Cp>That split is important because web services and batch jobs fail in different ways. If you want to watch data drift, service behavior, and job outcomes, this module gives a broad starting toolkit.\u003C\u002Fp>\n\u003Ccode>Web monitoring: Prometheus + Evidently + Grafana\nBatch monitoring: Prefect + MongoDB + Evidently\u003C\u002Fcode>\n\u003Ch2>6. Best practices for shipping ML\u003C\u002Fh2>\n\u003Cp>This module shifts from ML-specific tasks to the engineering habits that keep systems maintainable. It includes unit and integration testing, linting, formatting, pre-commit hooks, CI\u002FCD with GitHub Actions, and infrastructure as code with Terraform.\u003C\u002Fp>\n\u003Cp>For teams that already have a model but struggle with quality control, this is the section that ties everything together. It helps you build workflows that are easier to review, automate, and reproduce.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>Testing: unit and integration\u003C\u002Fli>\n  \u003Cli>Automation: GitHub Actions, pre-commit\u003C\u002Fli>\n  \u003Cli>Infrastructure: Terraform\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>7. Final project and certificate path\u003C\u002Fh2>\n\u003Cp>The final project asks you to combine the earlier modules into an end-to-end MLOps pipeline. That makes the course more than a video series, because you finish with a portfolio-ready system rather than isolated exercises.\u003C\u002Fp>\n\u003Cp>Certificates are available for learners who complete the final project during a live cohort, but the course is now fully available for self-paced study. The repository also points to Slack, Telegram, and the FAQ for support, which makes it easier to keep moving when you get stuck.\u003C\u002Fp>\n\u003Ch2>How to decide\u003C\u002Fh2>\n\u003Cp>Pick this course if you want a free, structured introduction to production ML and you already know enough Python and Docker to follow along. It is strongest for learners who need a practical map from experimentation to deployment and monitoring.\u003C\u002Fp>\n\u003Cp>If you mainly want a certificate, the live-cohort route matters. If you want the \u003Ca href=\"\u002Ftag\u002Fskills\">skills\u003C\u002Fa>, the self-paced path is enough: the materials, homework, and final project are all there in the repository.\u003C\u002Fp>","9 free modules show how to move from model training to deployment, monitoring, and a final project in MLOps Zoomcamp.","github.com","https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fmlops-zoomcamp",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781542983207-pzyb.png","industry","en","1ca3cf77-7688-45c3-ad99-ecf7c0ec7f54",[17,18,19,20,21,22,23,24],"MLOps","machine learning operations","MLflow","model deployment","model monitoring","workflow orchestration","GitHub","free course",[26,27,28],"The course is free, self-paced, and organized into 9 weeks of MLOps training.","It covers experiment tracking, orchestration, deployment, monitoring, and best practices.","The final project ties the modules together into a portfolio-ready pipeline.",0,"2026-06-15T17:02:28.963068+00:00","2026-06-15T17:02:28.957+00:00","d19fc184-5852-4c4d-9ec0-db0c4841ac17",{"tags":34,"relatedLang":45,"relatedPosts":49},[35,37,39,41,43],{"name":18,"slug":36},"machine-learning-operations",{"name":21,"slug":38},"model-monitoring",{"name":17,"slug":40},"mlops",{"name":20,"slug":42},"model-deployment",{"name":19,"slug":44},"mlflow",{"id":15,"slug":46,"title":47,"language":48},"mlops-zoomcamp-path-to-production-ml-zh","MLOps Zoomcamp 把模型帶上線的完整路線","zh",[50,56,62,68,74,80],{"id":51,"slug":52,"title":53,"cover_image":54,"image_url":54,"created_at":55,"category":13},"4f5b9071-650c-4376-80f2-1c33484d83cb","kalshi-adds-solana-perpetual-futures-after-xrp-en","Kalshi adds Solana perpetual futures after XRP","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781553769118-d4w7.png","2026-06-15T20:02:30.737686+00:00",{"id":57,"slug":58,"title":59,"cover_image":60,"image_url":60,"created_at":61,"category":13},"300b42e9-6fea-45f4-bc4a-664cb7244ade","mlops-is-not-optional-for-production-ml-en","MLOps is not optional if you want ML in production","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781543872489-ll37.png","2026-06-15T17:17:22.508357+00:00",{"id":63,"slug":64,"title":65,"cover_image":66,"image_url":66,"created_at":67,"category":13},"75ec77eb-424e-474f-813f-bb387da904e9","cloudflare-too-expensive-after-share-price-surge-en","Cloudflare Is Too Expensive to Buy After the Surge","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781539368511-x1fq.png","2026-06-15T16:02:19.031847+00:00",{"id":69,"slug":70,"title":71,"cover_image":72,"image_url":72,"created_at":73,"category":13},"f49d58f8-0bd5-4442-9bdb-b0ca12e97986","turbovec-cuts-10m-vector-ram-to-4gb-en","TurboVec cuts 10M-vector RAM to 4GB","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781528566106-frfj.png","2026-06-15T13:02:23.344662+00:00",{"id":75,"slug":76,"title":77,"cover_image":78,"image_url":78,"created_at":79,"category":13},"0423587b-197e-41cc-99d3-6197263e6874","midjourney-v8-1-default-model-update-en","Midjourney V8.1 now ships as default model","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781515062253-2i5e.png","2026-06-15T09:17:19.17797+00:00",{"id":81,"slug":82,"title":83,"cover_image":84,"image_url":84,"created_at":85,"category":13},"f862c145-269f-4ef4-aa12-44207a7475aa","midjourney-free-methods-vs-paid-access-en","Midjourney Free Methods vs Paid Access","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781514188185-dk6r.png","2026-06-15T09:02:35.461188+00:00",[87,92,97,102,107,112,117,122,127,132],{"id":88,"slug":89,"title":90,"created_at":91},"d35a1bd9-e709-412e-a2df-392df1dc572a","ai-impact-2026-developments-market-en","AI's Impact in 2026: Key Developments and Market Shifts","2026-03-25T16:20:33.205823+00:00",{"id":93,"slug":94,"title":95,"created_at":96},"5ed27921-5fd6-492e-8c59-78393bf37710","trumps-ai-legislative-framework-en","Trump's AI Legislative Framework: What's Inside?","2026-03-25T16:22:20.005325+00:00",{"id":98,"slug":99,"title":100,"created_at":101},"e454a642-f03c-4794-b185-5f651aebbaca","nvidia-gtc-2026-key-highlights-innovations-en","NVIDIA GTC 2026: Key Highlights and Innovations","2026-03-25T16:22:47.882615+00:00",{"id":103,"slug":104,"title":105,"created_at":106},"0ebb5b16-774a-4922-945d-5f2ce1df5a6d","claude-usage-diversifies-learning-curves-en","Claude Usage Diversifies, Learning Curves Emerge","2026-03-25T16:25:50.770376+00:00",{"id":108,"slug":109,"title":110,"created_at":111},"69934e86-2fc5-4280-8223-7b917a48ace8","openclaw-ai-commoditization-concerns-en","OpenClaw's Rise Raises Concerns of AI Model Commoditization","2026-03-25T16:26:30.582047+00:00",{"id":113,"slug":114,"title":115,"created_at":116},"b4b2575b-2ac8-46b2-b90e-ab1d7c060797","google-gemini-ai-rollout-2026-en","Google's Gemini AI Rollout Extended to 2026","2026-03-25T16:28:14.808842+00:00",{"id":118,"slug":119,"title":120,"created_at":121},"6e18bc65-42ae-4ad0-b564-67d7f66b979e","meta-llama4-fabricated-results-scandal-en","Meta's Llama 4 Scandal: Fabricated AI Test Results Unveiled","2026-03-25T16:29:15.482836+00:00",{"id":123,"slug":124,"title":125,"created_at":126},"bf888e9d-08be-4f47-996c-7b24b5ab3500","accenture-mistral-ai-deployment-en","Accenture and Mistral AI Team Up for AI Deployment","2026-03-25T16:31:01.894655+00:00",{"id":128,"slug":129,"title":130,"created_at":131},"5382b536-fad2-49c6-ac85-9eb2bae49f35","mistral-ai-high-stakes-2026-en","Mistral AI: Facing High Stakes in 2026","2026-03-25T16:31:39.941974+00:00",{"id":133,"slug":134,"title":135,"created_at":136},"9da3d2d6-b669-4971-ba1d-17fdb3548ed5","cursors-meteoric-rise-pressures-en","Cursor's Meteoric Rise Faces Industry Pressures","2026-03-25T16:32:21.899217+00:00"]