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Decision brief
awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Good fit when
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- Use if interested in cost optimization for ML infrastructure and CI/CD specific to AI.
Avoid when
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list.
- Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (621d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/visenger/awesome-mlopsHow it fits your stack(1)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Visenger maintains this repository which compiles resources related to deploying and serving machine learning models effectively under the umbrella of MLOps practices.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
1. [Deploying Python ML Models with Flask, Docker and Kubernetes](https://alexioannides.com/2019/01/1Source link
Tags
README
MLOps: Model Deployment and Serving
Click to expand!
- AI Infrastructure for Everyone: DeterminedAI
- Deploying R Models with MLflow and Docker
- What Does it Mean to Deploy a Machine Learning Model?
- Software Interfaces for Machine Learning Deployment
- Batch Inference for Machine Learning Deployment
- AWS Cost Optimization for ML Infrastructure - EC2 spend
- CI/CD for Machine Learning & AI
- Itaú Unibanco: How we built a CI/CD Pipeline for machine learning with online training in Kubeflow
- 101 For Serving ML Models
- Deploying Machine Learning models to production — Inference service architecture patterns
- Serverless ML: Deploying Lightweight Models at Scale
- ML Model Rollout To Production. Part 1 | Part 2
- Deploying Python ML Models with Flask, Docker and Kubernetes
- Deploying Python ML Models with Bodywork
- 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
- Efficient Machine Learning Inference. The benefits of multi-model serving where latency matters
- Deploying Hugging Face ML Models in the Cloud with Infrastructure as Code
MLOps: Infrastructure & Tooling
Click to expand!
- MLOps Infrastructure Stack Canvas
- Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps
- AI Infrastructure Alliance. Building the canonical stack for AI/ML
- Linux Foundation AI Foundation
- ML Infrastructure Tools for Production | Part 1 — Production ML — The Final Stage of the Model Workflow | Part 2 — Model Deployment and Serving
- The MLOps Stack Template (by valohai)
- Navigating the MLOps tooling landscape
- [MLOps.toy
For agents
This page has a .md twin and JSON over the API.