Overview

Layer	What it covers
10 steps (this article)	What practitioners actually do week by week
6 macro stages (GIF 1)	How teams talk about the lifecycle in planning meetings
7 GIFs	One visual per critical transition — some steps share a GIF

The six macro stages are: Problem → Data → Model → Train → Evaluate → Deploy. Steps 1–10 zoom in inside that frame.

0 — Deploy, monitor, and retrain

Package the model, ship an API, watch it in the wild.

Typical path:

Serialize artifact (v2.4.pkl, ONNX, SavedModel)
Containerize (Docker)
Deploy behind /predict on cloud or edge
Monitor latency, errors, input drift, prediction drift
Retrain or rollback when alerts fire

Deploy and monitor — production feedback loop

Models rot. User behavior shifts. Upstream schemas change. Monitoring is not optional — it’s Step 10 of the same pipeline.

The ten steps at a glance¶

#	Step	Macro stage	GIF
1	Define problem & KPIs	Problem	GIF 2
2	Feasibility check	Problem	—
3	Collect data	Data	GIF 3
4	Clean & explore (EDA)	Data	GIF 3
5	Feature engineering	Data	GIF 3
6	Split & leakage audit	Data	GIF 3
7	Choose model	Model	GIF 4
8	Train & tune	Train	GIF 5
9	Evaluate & test	Evaluate	GIF 6
10	Deploy & monitor	Deploy	GIF 7

Where the time actually goes¶

Phase	Steps	Typical calendar share
Planning	1–2	5–10%
Data	3–6	40–60%
Modeling	7–8	15–25%
Validation	9	5–10%
Production	10	10–20% (ongoing)

The algorithm (Steps 7–8) is often under a quarter of the work. The rest is clarity, data, engineering, and ops.

FAQ¶

Should my blog say 6 steps or 10?
Use 10 in the title for depth and SEO (“complete ML pipeline”). Mention the 6 macro stages once in the intro so readers who know MLOps diagrams still feel at home.

Do I need ten GIFs?
No. Seven is enough if Steps 3–6 share the data-prep funnel GIF and Steps 1–2 share the problem-framing GIF.

What’s the single most skipped step?
Step 2 (feasibility) and Step 6 (leakage audit). Skipping them causes the most expensive rework.

When do I stop training?
When validation metrics plateau and the model beats your Step 1 baseline on the metrics that matter for the product.

Publish checklist¶

[ ] Hero: blog-poster-1200x600.png (PNG, not GIF)
[ ] GIF 1 after intro paragraph
[ ] GIFs 2–7 under matching step sections
[ ] Meta description: Ten steps to build a machine learning model — from KPIs and data prep to training, evaluation, deployment, and monitoring.
[ ] LinkedIn: short hook in post; full URL in first comment

Regenerate assets¶

cd guides/ml-model-6-steps/assets
python3 render_blog_poster.py
python3 render_gif_01.py
python3 render_gif_02.py
python3 render_gif_03.py
python3 render_gif_04.py
python3 render_gif_05_07.py all

Pipeline overview

Six steps mix

— Define the problem and success metrics

Before notebooks open, write down what “done” looks like.

What decision should the model support?
What metric matches the cost of being wrong? (precision vs recall, MAE vs RMSE)
What baseline must you beat? (rules, majority class, last year’s manual process)

Deliverable: one-page brief — use case, constraints, KPIs, baseline.

Problem framing — vague vs sharp ML task

Step 2 — Run a feasibility check¶

Not every idea needs machine learning.

Ask plainly:

Do you have labeled examples (or a realistic labeling plan)?
Is the signal in the data, or is this a product/process fix?
Can you get to a MVP metric in the time you have?

If the answer is no, fix data collection or product scope first. Cheaper than training the wrong model.

Deliverable: go / no-go note with risks listed.

Step 3 — Collect raw data¶

Gather examples that match the problem you defined in Step 1.

Pull from warehouses, APIs, logs, human labelers, or public datasets
Track provenance — source, timestamp, version, PII rules
Store raw data immutable; never overwrite the source copy

Common mistake: training on a convenience sample that doesn’t match production traffic.

Step 4 — Clean and explore (EDA)¶

Open the data before you trust it.

Profile distributions, missing rates, duplicates, unit bugs
Plot labels over time — sudden shifts often mean pipeline breaks
Document findings; EDA notes become onboarding material later

Time spent here: often 20–40% of the project calendar. That’s normal.

Step 5 — Engineer features¶

Turn raw columns into signals the model can learn from.

Ratios, aggregates, encodings, text tokens, date parts, embeddings
Keep feature definitions in code (not one-off notebook cells)
Version feature logic with the same discipline as model weights

Rule: if you can’t explain a feature to a teammate, don’t ship it.

Step 6 — Split data and kill leakage¶

Lock your evaluation story before training hype starts.

Train / validation / test — test set stays sealed until the end
Time-based splits for forecasting; group splits when rows aren’t independent
Reject columns that encode the future (future_spend, post-outcome fields)

Data prep funnel — messy rows in, clean features out, leakage rejected

Deliverable: feature matrix + split indices + leakage audit checklist.

Step 7 — Choose a modeling approach¶

Match method to data size and interpretability — not Twitter hype.

Data	Need explanations?	Often start with
Small	Yes	Logistic regression, linear models
Large	Yes	Random Forest, GBT + SHAP
Small	No	Gradient boosting (XGBoost/LightGBM)
Large	No	Neural networks

Model selection matrix — data size × interpretability

Train the simplest candidate that could work. Upgrade only when validation metrics justify the complexity.

Step 8 — Train and tune¶

Feed prepared data to the model. Iterate until validation performance plateaus.

Loop:

Train on the training set
Measure on the validation set
Adjust hyperparameters or features
Repeat

Log every run: data hash, config, metrics, runtime. Reproducibility saves you during audits and regressions.

Train loop — validate, adjust, stop at plateau

Stop when: validation metric gains shrink below your noise floor — not when the leaderboard looks pretty.

Step 9 — Evaluate on the held-out test set¶

The test set gets one shot. No peeking during tuning.

Report metrics that match Step 1 KPIs
Slice by region, device, cohort, language — aggregates hide failures
Check fairness across groups; error clusters point back to Steps 4–6

Evaluation dashboard — slice analysis catches APAC failure

If a slice fails, don’t deploy and hope. Fix data or modeling, then re-run from Step 8.

Step 10 — Deploy, monitor, and retrain¶