AI & LLM Skill
Agent Eval Suite Langsmith
Production agent eval suite LangSmith dataset curation + Promptfoo assertion framework +…
$15
ML Pipeline Workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and…
A guide to building end-to-end MLOps pipelines from data preparation through training, validation, and production deployment. It covers DAG orchestration, experiment tracking, model registries, drift detection, and safe rollout patterns so model training and deployment become reproducible and automated.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category AI & LLM
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
Which data was this model trained on? If the answer takes more than one lookup, the pipeline is broken. This run keeps that answer cheap, from ingestion through drift-triggered retraining.
- Ingests raw data and gates it through quality checks (Great Expectations style validation), then versions the processed dataset with a DVC-style hash so every model can later answer the question: which data was this trained on.
- Runs feature engineering into a single feature store serving both training and inference. Training uses point-in-time correct features, serving reads the same store online, because separately built pipelines silently create train-serve skew.
- Orchestrates training as an idempotent DAG (Airflow, Dagster or Kubeflow): every task versions its inputs and outputs, fixes random seeds, checkpoints, and on failure restarts from the failed task only, never a full cascade rerun.
- Logs every run to the experiment tracker and model registry (MLflow or Weights and Biases): hyperparameters, data version hash, validation metrics and the training code commit SHA, so promotion means promoting the best registered metric, not whatever happens to be running.
- Deploys through shadow mode first, where the new model sees production traffic but only logs its answers, then a canary rollout of 5, 25, 50, 100 percent with automatic rollback the moment a metric regresses. A direct 100 percent cutover is forbidden.
- Monitors production for data drift by comparing live input distributions to the training reference with PSI, KS-test or Jensen-Shannon divergence, and triggers automated retraining when the threshold is crossed, watching concept drift separately because a stable input distribution does not guarantee a correct model.
One power source. 6 lines out.
ml-pipeline-workflow · core
core active · 6 lines
- ✓ building a new ml pipeline
Building a new ML pipeline from scratch
- ✓ designing dag-based orch…
Designing DAG-based orchestration for model training
- ✓ setting up reproducible
Setting up reproducible training with experiment tracking
- ✓ detecting data drift and
Detecting data drift and triggering automated retraining
- ✓ rolling out new models s…
Rolling out new models safely with shadow and canary deployment
- ✓ maintaining model lineage
Maintaining model lineage and rollback capability
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
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Make model training reproducible so you always know how a model was produced
license: perpetual -
Catch silent performance decay early with drift detection and monitoring
license: perpetual -
Roll out new models without risk using shadow and gradual canary releases
license: perpetual -
Roll back instantly with a model registry and versioned lineage
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
End-to-end pipeline architecture across six lifecycle stages
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
ML engineers and data teams building production pipelines who need reproducible training and safe, automated model deployment.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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Is it tied to one orchestrator like Airflow or Kubeflow?
No single tool is assumed: the DAG orchestration patterns, idempotency rules, and retry strategy are written to apply to whichever orchestrator you run. The lifecycle stages and registry discipline matter more than the scheduler brand.
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How does automated retraining actually get triggered?
Data-drift detection runs against statistical thresholds, and crossing one fires a retrain trigger instead of waiting for someone to notice decayed predictions. Monitoring covers the silent case where the model still responds but quality slips.
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Will it make my model more accurate?
No. It makes training reproducible and deployment safe through shadow, canary, and blue-green rollout with rollback. Model architecture, feature engineering, and accuracy work stay your job.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.