A notebook proves something can work. A product proves it works reliably, for most users, under constraints (latency, cost, security), and with change control.
The gap between the two is where many teams stall. The good news: the gap is predictable. Use the checklist below to move from prototype to a production-grade ML/LLM feature without over-engineering.
1) Define the feature boundary and success criteria
Start by writing down:
- What user problem the model solves
- The specific decision or output it produces
- What happens when the model is uncertain
- How you’ll measure success (online and offline)
Avoid vague goals like “better answers.” Prefer:
- “Reduce average handle time by 15% on category X”
- “Extract fields with ≥ 98% schema validity and ≥ 95% exact match”
2) Lock the I/O contract (and keep it stable)
Your model is a dependency. Dependencies need contracts.
- Input contract: fields, types, required vs optional
- Output contract: schema, allowed enums, units, and formatting
- Error contract: what errors look like and how callers should handle them
If a downstream system consumes the output programmatically, do not ship free-form text. Validate structured output and reject invalid payloads.
3) Make data a first-class artifact
Most “model issues” are data issues.
Training/label data checklist
- Source of truth documented (where labels come from)
- Labeling guidelines written and versioned
- Inter-annotator agreement measured (when applicable)
- Known blind spots documented
Feature data checklist
- Data definitions (what each field means)
- Null and missing-value semantics
- Time boundaries (what is known at prediction time)
- PII classification and retention policy
4) Build an evaluation suite before you deploy
An evaluation suite is a set of tests for model behavior. Without it, you’ll ship regressions.
Minimum viable eval:
- A golden set of representative examples
- A hard-case set (edge cases that break naive approaches)
- Metrics aligned to the task:
- classification: precision/recall, ROC-AUC
- extraction: exact match, F1, schema validity
- ranking: NDCG, MRR
- LLM outputs: format validity, groundedness, refusal correctness
Make evaluation reproducible: fixed dataset versions, fixed scoring scripts, logged model/config versions.
5) Decide your serving shape: batch, online, or hybrid
Different workloads need different architectures.
- Batch: cheapest; good for nightly scoring, analytics, offline workflows
- Online: low-latency; needed for interactive UX
- Hybrid: precompute embeddings/baselines; online for final step
Force a decision early. “We’ll do both” often means neither is done well.
6) Wrap the model behind a service boundary
Even if the model runs in-process today, design for a service boundary:
- HTTP/gRPC endpoint
- Request validation
- Authn/authz
- Timeouts and cancellation
- Structured logs
This makes it possible to scale, roll back, and route traffic without rewriting product code.
7) Treat prompts, configs, and artifacts as versioned dependencies
For LLM applications, your “model” includes:
- Prompt templates
- Retrieval configuration (chunking, top-k, filters)
- Tool definitions
- Post-processing and validators
For classical ML, it includes:
- Feature transformations
- Model weights
- Calibration layers
Version everything. Persist:
- model ID
- dataset version
- code commit
- prompt/config version
When a user reports a bad result, you want to reconstruct the exact pipeline.
8) Add guardrails: validation, calibration, and uncertainty handling
Production systems need predictable behavior.
- Validate inputs (type, range, allowed values)
- Validate outputs (schema, bounds, allowed enums)
- Handle uncertainty:
- thresholding for classification
- abstain/deferral paths
- “ask a clarifying question” for LLM UX
If an output can cause harm (financial, security, compliance), require human review or add deterministic checks.
9) Plan rollouts like any other risky change
Use progressive delivery:
- Shadow mode (log-only)
- Internal dogfood
- Small percentage rollout
- Ramp by cohorts
Track metrics that matter during rollout:
- task success
- latency and error rate
- cost per request
- escalation/deferral rate
- user complaints linked to the feature
Include a kill switch.
10) Operational readiness: latency, cost, and failure modes
Your notebook didn’t have P95 latency constraints or unpredictable spikes.
Checklist:
- Timeouts and retries defined (and capped)
- Rate limits (per user and per org)
- Caching strategy (embeddings, retrieval results, common responses)
- Cost ceilings and routing (cheap vs strong model)
- Backpressure behavior (queueing vs fail fast)
For LLM systems, measure token usage and enforce budgets per request.
11) Security and privacy are not optional
Common misses:
- Logging raw user prompts containing PII
- Sending sensitive data to third-party APIs without controls
- Storing model outputs indefinitely
Do the basics:
- Classify data and redact where necessary
- Minimize what you send to the model
- Encrypt at rest and in transit
- Audit who can access prompts, traces, and labels
12) Close the loop: monitoring and continuous improvement
After launch, the work changes—but doesn’t stop.
Monitor:
- Data drift (input distributions)
- Performance drift (label-based when available)
- Schema failure rate (LLMs)
- Concept drift by segment (region, customer tier, language)
Build a lightweight feedback pipeline:
- Capture bad outcomes with context
- Label root causes
- Add to hard-case evals
- Re-test before shipping changes
A compact “ship it” gate
If you need a one-page gate, use this:
- Contract: typed I/O + output validation
- Eval: golden set + regression gate in CI
- Ops: metrics for latency, errors, cost; kill switch
- Rollout: shadow → 1% → ramp
- Security: data classification + redaction + access controls
Closing
The notebook-to-product journey is mostly about reducing ambiguity: in data, interfaces, evaluation, and operations. Use this checklist as a forcing function.
Once you can ship one model safely, you can ship many—because you’ve built the system that makes ML a repeatable part of engineering, not a one-off experiment.