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awesome-mlops

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visenger/awesome-mlops

A curated list of references for MLOps

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Created Mar 3, 2020

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Overview

A curated list of references for MLOps

Capability facts

No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).

Categories

Vector DatabasesModel TrainingInference & Serving

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Jul 11, 2026)

1. [Deploying Python ML Models with Flask, Docker and Kubernetes](https://alexioannides.com/2019/01/1
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Tags

engineeringdata-sciencemlaimachine-learningfederated-learningmlopsdevops

README

MLOps: Model Deployment and Serving

Click to expand!
  1. AI Infrastructure for Everyone: DeterminedAI
  2. Deploying R Models with MLflow and Docker
  3. What Does it Mean to Deploy a Machine Learning Model?
  4. Software Interfaces for Machine Learning Deployment
  5. Batch Inference for Machine Learning Deployment
  6. AWS Cost Optimization for ML Infrastructure - EC2 spend
  7. CI/CD for Machine Learning & AI
  8. Itaú Unibanco: How we built a CI/CD Pipeline for machine learning with online training in Kubeflow
  9. 101 For Serving ML Models
  10. Deploying Machine Learning models to production — Inference service architecture patterns
  11. Serverless ML: Deploying Lightweight Models at Scale
  12. ML Model Rollout To Production. Part 1 | Part 2
  13. Deploying Python ML Models with Flask, Docker and Kubernetes
  14. Deploying Python ML Models with Bodywork
  15. Framework for a successful Continuous Training Strategy. When should the model be retrained? What data should be used? What should be retrained? A data-driven approach
  16. Efficient Machine Learning Inference. The benefits of multi-model serving where latency matters
  17. Deploying Hugging Face ML Models in the Cloud with Infrastructure as Code

MLOps: Infrastructure & Tooling

Click to expand!
  1. MLOps Infrastructure Stack Canvas
  2. Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps
  3. AI Infrastructure Alliance. Building the canonical stack for AI/ML
  4. Linux Foundation AI Foundation
  5. ML Infrastructure Tools for Production | Part 1 — Production ML — The Final Stage of the Model Workflow | Part 2 — Model Deployment and Serving
  6. The MLOps Stack Template (by valohai)
  7. Navigating the MLOps tooling landscape
  8. [MLOps.toy