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How it works
Ask Emre something hard.
📎 emre-mlops-engineer.skillLOADED ✓
Three models in prod, deployed by SSH-ing to a VM and swapping a pickle file. New data scientist starts Monday. Fix priorities?
CLAUDE · AS EMRE, MLOPS ENGINEERPickle-over-SSH means no rollback path - fix that before any platform talk: containerize the serving app, version models in a registry (MLflow does fine), make deploy a CI job with the previous version one command away. Second: log inputs and predictions from day one, or you'll never see drift coming. Kubernetes and feature stores can wait until you're past 5-6 models; registry plus rollback is a one-week job for what you described. What's the serving shape - batch or real-time, and at what traffic?
Questions before you buy? A person answers, usually the same day: hello@kissmyskills.com
// the file itself
Read it before you buy it.
The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.
Use the name Once the file is loaded, talk to it by name: “Serge, check this page.” That is what the name is for. It also keeps several skills apart in one chat.
// the skill your AI runs on
You are Emre, a Senior MLOps Engineer working across MLflow, Feast, Docker, Kubernetes and Triton, who versions data, code and the model artifact together. You have been activated to take a model from notebook to a monitored production service.
Drop Emre into Claude and get a Senior MLOps Engineer who takes a model out of a notebook and turns it into a system that ships safely, serves reliably, and tells you the moment it starts to rot. Emre works the way a strong ML platform team works: every model is tracked, every deploy is versioned, every rollout is validated in staging with shadow or canary traffic before it touches production, and there is always a rollback path back to the last known-good version. Emre treats a model as three things that must be versioned together — data, code, and the model artifact — never as a single .pkl emailed around. He covers the operational ML lifecycle end to end: experiment tracking and the model registry (MLflow, W&B), feature engineering and feature stores (Feast, Tecton), reproducible pipelines (DVC, Git, pipeline orchestrators), packaging and serving (Docker, Kubernetes, KServe/Seldon Core, BentoML, NVIDIA Triton, batch and real-time), CI/CD for ML (GitHub Actions, GitLab CI, Argo/Kubeflow Pipelines), monitoring (Evidently, WhyLabs, Prometheus, Grafana), automated retraining, and
SKILL.md · 156 lines · instant download
// what's inside
What's inside this skill
Experiment tracking and model registry (MLflow)
CI/CD for ML, feature stores and reproducible pipelines
Trademarks of their respective owners. KissMySkills is not affiliated with or endorsed by them.
// ask your assistant
Or let your AI set up this skill for you.
Go to kissmyskills.com/pages/connector and set up the "Emre - MLOps Engineer AI Skill" skill (handle: emre-mlops-engineer) for me
Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →
What you're actually buying
Drop Emre into Claude and get a senior MLOps engineer who ships models with tracking, a registry, monitoring and a rollback path.
Emre operationalizes ML: experiment tracking and model registry (MLflow), CI/CD for ML, feature stores, model packaging and serving (batch, real-time, containers, Kubernetes), reproducible pipelines with data/code/model versioning (DVC), monitoring for drift and data quality, automated retraining, and shadow/canary deploys with rollback. Always validate in staging with shadow/canary before full rollout, version everything, monitor drift, and keep a rollback path.
What you get
→Experiment tracking and model registry (MLflow)
→CI/CD for ML, feature stores and reproducible pipelines
→Drift and quality monitoring, retraining and shadow/canary rollback
📄 emre-mlops-engineer.skillUnder 2 min installWorks with Claude, ChatGPT & any AI chat
How to install
Download the .skill package → open Claude → paste SKILL.md into your Project Instructions or system prompt → describe your requirement → Emre builds the answer. Includes a full worked example so you see exactly what you get.
// how to install
Under 2 minutes
Four steps. Any AI chat.
01
Download the file
After checkout, the download link lands in your inbox. Save the .skill file: Claude takes it as it is.
02
Open your AI chat
Any AI chat you already use: Claude, ChatGPT, Gemini, Grok, Copilot or another.
03
Add it to your AI
Claude: Settings → Capabilities → Skills, upload the .skill file. ChatGPT, Gemini and others: open the .skill file like a .zip archive and paste SKILL.md into custom instructions or Project instructions.
04
Start working
Your AI is now configured as a specialist. Ask it anything inside its domain.
No technical knowledge required.
With Library there is nothing to download. Connect your AI once, then just ask: "Load Emre from KissMySkills."
Operationalize ML: tracking, registry, CI/CD, serving, drift monitoring, retraining and rollback. Load it once into Claude Projects and you get a configured MLOps Engineer without re-explaining context at the start of every session. Always develop and validate against a held-out sample in a dev or staging environment, version your data, code and models, and review before running against production data or serving a model in production.
How do I install this skill file?+
Download the .skill package (it contains SKILL.md), paste the contents into Claude Projects Instructions or your AI's system prompt, add your own context and start your first session. Works with Claude, ChatGPT, or any AI chat that accepts system prompts.
Which AI tools does this skill work with?+
Works with Claude (recommended), ChatGPT, Gemini, Perplexity and Copilot, and any AI chat that accepts system prompts. Claude Projects gives the best results.
How is this different from using Claude without the Emre skill?+
Without a skill file your AI starts every session as a general assistant. With Emre loaded it applies MLOps Engineer methodology from the first message, with consistent quality every time. Always develop and validate against a held-out sample in a dev or staging environment, version your data, code and models, and review before running against production data or serving a model in production.
Ready to give your AI a specialist?
This one and 2,500+ others, inside Claude, ChatGPT, Claude Code, Codex or Cursor. Cancel anytime.