{"repo":"laban254/ml-for-infrastructure","free":true,"listed":false,"github":"https://github.com/laban254/ml-for-infrastructure","clone":"git clone https://github.com/laban254/ml-for-infrastructure.git","description":"ML & deep learning notebooks for SRE/DevOps — anomaly detection, log clustering, drift monitoring, LLM fine-tuning. Runs in Colab, no install needed.","language":"Jupyter Notebook","stars":27,"topics":["anomaly-detection","data-science","deep-learning","jupyter-notebook","machine-learning","mlops","observability","pytorch","scikit-learn","sre"],"license":null,"category":"machine-learning","readme_excerpt":"ML for Infrastructure --- What this is 27 Jupyter notebooks that teach machine learning through real infrastructure scenarios — not Iris, not Titanic. Every notebook uses data engineers actually deal with: CPU spikes, log streams, latency distributions shifting after a deploy, and model drift in production. If this is useful, a star helps others find it. --- Documentation - Getting Started — 5-minute quickstart - Setup & Installation — detailed installation - Architecture — project structure and design - Contributing — how to contribute --- Capabilities Intelligent Monitoring ( 03 machine learning/ & 05 sre applications/ ) Anomaly Detection: Isolation Forest on Prometheus-style CPU metrics — catches DDoS spikes and service outages without manual thresholds. Includes a live sensitivity slider. Log Clustering: TF-IDF + KMeans groups unstructured logs by pattern, auto-names each cluster from its top keywords, and flags log lines that don't fit any known group. Predictive Scaling: Regression model forecasts latency from connection-spike data before the slowdown hits users. MLOps & Production Pipelines ML is only useful if it runs reliably in production. Leakage-Free Pipelines: Preprocessing and model bundled together so training and serving use identical transformations. Precision over Accuracy: Notebooks prioritize recall (missed outages) and precision (alert fatigue) — the metrics that matter on-call, not just overall accuracy. Hyperparameter Tuning: GridSearchCV sweep over API","default_branch":null,"files":null,"tree":[],"storefront":"/r/laban254","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/laban254/ml-for-infrastructure/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}