ayush488-glitch/mlops-stack

mlops-agent-workflow

Anti-slop agentic engineering co-pilot. Teaches the Research-Plan-Implement (RPI) workflow, context management, quality gates, per-agent isolation, and anti-slop patterns for building software with AI coding agents. Produces agent-workflow.md or project…

原文语言：英语

mlops-code-review

Full software engineering and ML-specific code review co-pilot. Reviews Python code for quality, security, testing, type safety, and ML-specific issues including data leakage, training-serving skew, feature engineering smells, and reproducibility. Produces…

原文语言：英语

mlops-system-design

System design co-pilot covering both general distributed systems and ML-specific infrastructure. Guides users through API design, database design, scalability, reliability, ML serving patterns, feature stores, training pipelines, and ML platform architecture.…

原文语言：英语

mlops-tabular

Production-grade MLOps co-pilot for tabular data. Guides users end-to-end from business problem through system design, implementation, deployment, and monitoring. Adapts dynamically to the user's specific problem, dataset, constraints, and chosen…

原文语言：英语

mlops-architecture

Deep-dive MLOps architecture design for tabular data. Walks through all 9 sub-phases of system design: full pipeline explanation (10 stages, 5 pipelines, maturity levels), data plan, feature plan, training plan, deployment plan, monitoring plan, versioning…

原文语言：英语

mlops-data-and-features

Deep-dive data foundation and feature engineering for tabular ML. Covers project setup, data loading with validation, EDA, and preprocessing (null handling, scaling with formulas, categorical encoding with target encoding smoothing, training-serving skew…

原文语言：英语

mlops-deploy-monitor

Deep-dive deployment, monitoring, and production hardening for tabular ML. Covers drift detection (data vs concept drift, KS/Chi-squared/PSI/Wasserstein with thresholds), deployment strategies (shadow/canary/blue-green/A-B), four-layer monitoring ladder,…

原文语言：英语

mlops-problem-framing

Deep-dive problem framing for tabular ML. Guides users through the six-word ML suitability test, three legitimate paths (Build ML / Rules / Not Now), problem statement template, metric ladder, seven discovery questions, and six forcing questions. Produces…

原文语言：英语

mlops-training-eval

Deep-dive model training and evaluation for tabular ML. Covers experiment tracking with four reproducibility elements, baseline models as mandatory, evaluation with slice-level analysis and confidence intervals via bootstrapping, and class imbalance handling…

原文语言：英语