Implementing MLOps
Ship ML models to production: experiment tracking, model registry, CI/CD pipelines, data/model drift monitoring, feature stores, and MLOps maturity progression
- What
- Ship ML models to production: experiment tracking, model registry, CI/CD pipelines, data/model drift monitoring, feature stores, and MLOps maturity progression
- Cost
- Free
- Needs
- an ML project to deploy — tooling of your choice (MLflow or W&B for tracking, Docker/Kubernetes or serverless for serving, a cloud account for infrastructure); code editors and CLIs as needed
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @ancoleman's implementing-mlops skill, listed here with credit to its creator: a comprehensive guide to taking machine learning models into production environments. It covers experiment tracking (MLflow, Weights & Biases, custom logging), model registry (versioning, approval workflows, model cards), CI/CD pipelines for ML (GitHub Actions, Jenkins, GitLab CI with ML-specific testing), deployment strategies (batch vs real-time, canary/blue-green, edge deployment), monitoring and observability (data and model drift detection, performance monitoring, alerting), feature stores (Feast, Tecton, custom solutions), infrastructure (Kubernetes, Docker, serverless), and MLOps maturity models — with production-ready templates, code examples (TensorFlow and PyTorch), an end-to-end example (scikit-learn to REST API to Kubernetes), and checklists for pre-deployment and production readiness. Honest caveats: infrastructure and tooling are yours — cloud platforms (AWS/GCP/Azure), Kubernetes clusters, and feature-store services are billed separately and need your own accounts; the skill is the workflow patterns and checklists, not the infrastructure; no guarantees against production failures — it teaches you to monitor them. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: an ML project to deploy — tooling of your choice (MLflow or W&B for tracking, Docker/Kubernetes or serverless for serving, a cloud account for infrastructure); code editors and CLIs as needed Install "Implementing MLOps" for me. It gives my agent @ancoleman's production-ML guide: experiment tracking, model registry with approval workflows, ML-specific CI/CD pipelines, deployment strategies (batch, real-time, canary, blue-green, edge), drift and performance monitoring, feature stores, infrastructure patterns (Kubernetes, Docker, serverless), MLOps maturity models — with production templates, TensorFlow and PyTorch examples, an end-to-end scikit-learn → REST API → Kubernetes example, and pre-deployment checklists. MIT licensed. Repository: https://github.com/ancoleman/ai-design-components/blob/main/skills/implementing-mlops/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and templates for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code — cloud credentials come only from my own vault/env. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "implementing-mlops". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. choose my tracking and serving stack; bring my own cloud account and Kubernetes cluster; run the pre-deployment checklist on my model before launch). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.