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Checklist · Model Serving

Model Serving Launch Checklist for 2026

Use this launch checklist to guide your model serving effort in 2026. Tasks are grouped into phases and prioritized so you always know what to do next. Start with [launch guides](/resources/launch-guides) for deeper context on each phase.

9 checklist items Updated from migrated LaunchTry SEO content

Phase 01

Foundation

3 tasks
  • c1
    critical1 day

    Define goals and KPIs (Model Serving)

    Document KPIs — inference latency, throughput, accuracy drift and user adoption rates — that define success for your model serving infrastructure at launch.

  • c2
    high2-3 days

    Identify target audience (Model Serving)

    Map out your earliest users: ML engineers doing evaluations, application teams needing predictions, or data scientists tracking model performance in production.

  • c3
    high2-3 days

    Audit current state (Model Serving)

    Inventory your current setup: existing inference endpoints, model formats (ONNX, TensorFlow), feature stores and monitoring gaps that the new system must address.

Phase 02

Execution

3 tasks
  • c4
    critical1 day

    Prioritize high-impact tasks (Model Serving)

    Rank tasks by blast radius and risk: prioritize robust inference, fallback routing and error handling before adding multi-model orchestration or auto-scaling features.

  • c5
    critical1 day

    Assign owners and deadlines (Model Serving)

    Assign ownership to engineers and set milestone dates for model optimization, deployment pipeline setup and inference service testing.

  • c6
    high2-3 days

    Set up tracking (Model Serving)

    Instrument observability: latency histograms, inference request counts, error rates and model performance metrics that reveal problems before users do.

Phase 03

Launch & Review

3 tasks
  • c7
    high2-3 days

    Ship and verify (Model Serving)

    Validate end-to-end inference paths in production, confirm model predictions match expectations and test graceful degradation when models are slow or unavailable.

  • c8
    high2-3 days

    Measure against KPIs (Model Serving)

    Compare inference latency and accuracy against your defined KPIs; identify bottlenecks in preprocessing, tokenization or model loading that slow real-world predictions.

  • c9
    critical1 day

    Iterate on results (Model Serving)

    Gather logs from the first week of serving, find patterns in slow models or failed requests and ship targeted fixes before scaling to more users.

Pro tips

  • Tackle critical items first
  • Review the checklist weekly
  • Adapt phases to your model serving context