deploy-ml-model-serving SKILL.md skillA
deploy-ml-model-serving from pjt222/agent-almanac is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add pjt222/agent-almanac/deploy-ml-model-serving@v1.0Pins v1.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- pjt222/agent-almanac
- Path
i18n/caveman-lite/skills/deploy-ml-model-serving/SKILL.md- Versions recorded
- 3
- Latest version
- v1.0, committed Aug 17, 2026
- First version recorded
- Apr 19, 2026
- Size
- 13,600 bytes
- Repository stars
- 43
- License
- MIT
Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Deploy ML Model Serving > See [Extended Examples](references/EXAMPLES.md) for complete configuration files and templates. Deploy machine learning models to production with scalable serving infrastructure, monitoring, and A/B testing. ## When to Use - Deploying trained models to production for real-time inference - Setting up REST or gRPC APIs for model predictions - Implementing autoscaling for variable load patterns - Running A/B tests between model versions - Migrating from batch to real-time inference - Building low-latency prediction services - Managing multiple model versions in production ## Inputs - **Required**: Registered model in MLflow Model Registry or trained model artifact - **Required**: Kubernetes cluster or container orchestration platform - **Required**: Serving framework choice (MLflow, BentoML, Seldon Core, TorchServe) - **Optional**: GPU resources for deep learning models - **Optional**: Monitoring infrastructure (Prometheus, Grafana) - **Optional**: Load balancer and ingress controller ## Procedure ### Step 1: Deploy with MLflow Models Serving Use MLflow's built-in serving for quick deployment of scikit-learn, PyTorch, and TensorFlow models. …
Read the whole file at its exact version.
How to install
mdr add pjt222/agent-almanac/deploy-ml-model-serving@sha256:78b4995cb99094d5The line at the top of this page pins the label v1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_qaruxlnmrzqprkt6)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 3 versions of this file, between Apr 19, 2026 and Aug 17, 2026, about one every 60 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0 latest | 2026-08-17 | a784899 | 13,600 B | A | view · diff |
| v1.0 | 2026-04-23 | eb36d45 | 13,571 B | A | view · diff |
| v1.0 | 2026-04-19 | e334ebd | 13,626 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (13600 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
pjt222/agent-almanac · 43 stars · license MIT · pushed 2026-11-06 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_qaruxlnmrzqprkt6 GET https://markdownregistry.com/api/v1/resolve?ref=pjt222/agent-almanac/deploy-ml-model-serving GET https://markdownregistry.com/api/v1/blob/78b4995cb99094d58f687f2d5e0e4daf79cc58a6a75271ad3584ee705d6c0e28
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.