series

100 Days of MLOps

A beginner-first, project-based path that takes you from "what is MLOps?" to running an end-to-end machine-learning platform on your own laptop. No computer-science background required and no cloud bills ever: every day explains the idea in plain English, builds one small working project, and runs 100% locally on macOS, Linux or Windows. You start with a clean environment and real ML, then layer on reproducibility and data/model versioning (Git + DVC), experiment tracking (MLflow), data validation and feature stores, model packaging and serving (FastAPI, Docker, BentoML), pipeline orchestration (Prefect), CI/CD for ML, Kubernetes deployment on a local cluster (kind), and finally monitoring, drift detection and automated retraining — capped by a capstone that ties all ten modules into one runnable platform.

1/100 days published ~0h total Absolute Beginner → MLOps Engineer
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Your progress starts here

1 published · ~0h of hands-on builds · sign in to sync progress across devices

01Foundations & Your Local MLOps Lab

What MLOps really is, a bulletproof cross-OS setup, Python environments and Git for ML, your first end-to-end model, clean project structure, and reproducibility from day one.

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  1. D01What is MLOps, and Setting Up Your Machine on Any OS19 min read

02Machine Learning You Can Operationalize

Just enough honest, practical ML to have real models worth shipping — data handling, leak-free splits, metrics, sklearn pipelines, cross-validation, tuning and packaging a training run.

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    03Reproducibility & Versioning: Data + Code

    Version everything with Git and DVC — datasets, pipelines, params and models — so any result can be reproduced from a clean clone.

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      04Experiment Tracking with MLflow

      Never lose a result again: track params, metrics and artifacts, compare runs, autolog, tune with Optuna, and run a local tracking server.

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        05Data Quality, Validation & Feature Stores

        Catch bad data before it breaks models with Pandera and Great Expectations, profile and document datasets, and serve consistent features with Feast.

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          06Packaging & Serving Models

          Turn a model into a real service — FastAPI, Pydantic validation, Docker, tests, batch vs online, BentoML, load testing and ONNX.

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            07Orchestration & Automated Pipelines

            Pipelines that run themselves with Prefect — retries, caching, scheduling, parameterization and an end-to-end training pipeline (plus Airflow awareness).

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              08CI/CD for Machine Learning

              Ship model changes safely: test ML code and models, GitHub Actions, continuous training, CML reports, validation gates and registry promotion.

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                09Deploying Models on Kubernetes, Locally

                Real serving infra on a local kind cluster — deploy, configure, scale, KServe/Seldon, canary and A/B, rollbacks and Argo CD GitOps.

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                  10Monitoring, Drift & the Full MLOps Loop

                  Models rot — detect it and respond: Prometheus/Grafana, prediction logging, data and concept drift with Evidently, alerting, automated retraining, governance, and the final capstone.

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                    11Coming up

                    One new day at a time — follow @syssignals to catch each release.

                    1. D02The MLOps Lifecycle & Mental Modelsoon
                    2. D03Python Environments for MLsoon
                    3. D04Git Basics for ML Projectssoon
                    4. D05Notebooks vs Scriptssoon
                    5. D06Your First ML Model, End to Endsoon
                    6. D07Saving & Loading Modelssoon
                    7. D08Project Structure That Scalessoon
                    8. D09Task Automation, Cross-OSsoon
                    9. D10Reproducibility 101soon
                    10. D11Working with Data: pandas for MLsoon
                    11. D12Train/Validation/Test Splitssoon
                    12. D13Classification Models & Metricssoon
                    13. D14Regression Models & Metricssoon
                    14. D15Feature Engineering with sklearn Pipelinessoon
                    15. D16Cross-Validation & Honest Evaluationsoon
                    16. D17Hyperparameters & Tuningsoon
                    17. D18Handling Imbalanced & Messy Datasoon
                    18. D19Model Interpretability Basicssoon
                    19. D20Packaging a Training Runsoon
                    20. D21Why Data Versioning? The Problemsoon
                    21. D22Intro to DVCsoon
                    22. D23DVC Remotes (Local)soon
                    23. D24DVC Pipelinessoon
                    24. D25Reproducing & Comparing Runssoon
                    25. D26Config Management with YAML/Hydrasoon
                    26. D27Environment Reproducibilitysoon
                    27. D28Versioned Data & Model Artifactssoon
                    28. D29Project Templatessoon
                    29. D30Capstone: A Fully Reproducible Projectsoon
                    30. D31Why Experiment Tracking?soon
                    31. D32MLflow Tracking Basicssoon
                    32. D33Comparing Runs in the MLflow UIsoon
                    33. D34Autologgingsoon
                    34. D35Logging Models & Artifactssoon
                    35. D36Hyperparameter Tuning, Tracked (Optuna)soon
                    36. D37MLflow Projectssoon
                    37. D38Organizing Experimentssoon
                    38. D39A Local Tracking Serversoon
                    39. D40Capstone: An Experimentation Workflowsoon
                    40. D41Why Data Validation Matterssoon
                    41. D42Schema Validation with Panderasoon
                    42. D43Great Expectationssoon
                    43. D44Validation as a Pipeline Gatesoon
                    44. D45Data Profiling & Documentationsoon
                    45. D46Reusable Feature Pipelinessoon
                    46. D47Intro to Feature Stores (Feast)soon
                    47. D48Online vs Offline Featuressoon
                    48. D49Preventing Training/Serving Skewsoon
                    49. D50Capstone: A Validated Feature Pipelinesoon
                    50. D51From Model to Inference APIsoon
                    51. D52Serving a Model with FastAPIsoon
                    52. D53Request/Response Validation with Pydanticsoon
                    53. D54Dockerizing Your Model Servicesoon
                    54. D55Testing Your Model APIsoon
                    55. D56Batch vs Online Inferencesoon
                    56. D57Model Serving with BentoMLsoon
                    57. D58Latency & Load Testingsoon
                    58. D59Optimizing Models with ONNXsoon
                    59. D60Capstone: A Production-Style Model Servicesoon
                    60. D61Why Orchestration? Cron Isn't Enoughsoon
                    61. D62Intro to Prefectsoon
                    62. D63Retries, Caching & Loggingsoon
                    63. D64Scheduling Pipelinessoon
                    64. D65Parameterized Pipelinessoon
                    65. D66An End-to-End Training Pipelinesoon
                    66. D67Data Pipelines & Dependencies (DAGs)soon
                    67. D68Airflow Awarenesssoon
                    68. D69Pipeline Observabilitysoon
                    69. D70Capstone: An Automated, Scheduled Pipelinesoon
                    70. D71Testing ML Codesoon
                    71. D72Testing Data & Modelssoon
                    72. D73CI with GitHub Actionssoon
                    73. D74Continuous Training Conceptssoon
                    74. D75CML: Continuous ML Reportssoon
                    75. D76Model Validation Gatessoon
                    76. D77The Model Registry as a Promotion Toolsoon
                    77. D78Building & Publishing Artifacts in CIsoon
                    78. D79GitOps for MLsoon
                    79. D80Capstone: A CI/CD Pipeline for a Modelsoon
                    80. D81Why Kubernetes for ML Serving? + kindsoon
                    81. D82Deploying Your Model API to kindsoon
                    82. D83Config & Secrets for Model Servicessoon
                    83. D84Scaling Model Serverssoon
                    84. D85Model Serving with KServe/Seldonsoon
                    85. D86Canary & Shadow Deploymentssoon
                    86. D87A/B Testing Modelssoon
                    87. D88Rollouts & Rollbackssoon
                    88. D89GitOps Model Deploys with Argo CDsoon
                    89. D90Capstone: A Model Serving Platform on kindsoon
                    90. D91Why Monitoring ML Is Differentsoon
                    91. D92Service Monitoring with Prometheus & Grafanasoon
                    92. D93Logging Predictions & Ground Truthsoon
                    93. D94Data Drift Detection with Evidentlysoon
                    94. D95Concept Drift & Performance Decaysoon
                    95. D96Alerting on Drift & Degradationsoon
                    96. D97Automated Retraining Triggerssoon
                    97. D98Governance, Lineage & Model Cardssoon
                    98. D99The Full MLOps Architecturesoon
                    99. D100Capstone: Your End-to-End Local MLOps Platformsoon