JAX PathwaysAll career routesCareer learning guide
Inference & deployment engineer
Package a trained model so another process or device can use it reliably. Own preprocessing, exported predictions, precision choices and measurements under a defined request workload.
The work
A converted model passes one example but fails on a changed shape or rare class. Locate the mismatch before comparing speed.
Preparation
Begin with a trained model and a reproducible prediction. You will need array shapes, saved state and a baseline measurement before reasoning about conversion, quantization or capacity.
Your portfolio
A deployment dossier with parity tests, precision tradeoffs, target-runtime measurements and rollback criteria.
Skills and evidence
JAX, Keras, TensorFlow and PyTorch interoperability
Required route practice
Locate the first numerical disagreement and reload the mapped artifact.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 1, 2, 3.
JAX export and artifact verification
Required route practice
Reload exported bytes and reject incompatible input signatures.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 2.
Weight, activation and accumulation precision
Required route practice
Account for weights, activations, accumulator and scales separately.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 2.
Edge deployment and capacity analysis
Required route practice
Separate observed CPU timing, queue simulation and unqualified edge-device measurements.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 3.
Containerized delivery and release evidence
Optional project extension; 3 project preparation lessons lie outside the required route
Verify model identity, readiness and release/rollback gates; real container/runtime qualification is separate.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 4.
Milestones
Build the shared CPU foundation
A reproducible regression fit with checked gradients and explicit random state.
Produce a trained artifact
A reproducible classifier
Reproduce its saved state
An interrupted run restored with its full state
Measure a reference workload
A trace-backed before-and-after performance report
Verify serving and capacity
An inference artifact and capacity plan
Review your readiness
Reproduce exported predictions and defend the serving plan under a clearly stated workload.
- Do preprocessing, weight layout and output meanings agree across the source and exported runtimes?
- What changed when you reduced weight, activation or accumulation precision?
- Which latency and throughput measurements support the capacity plan, and which device claims remain unmeasured?
- What checks would reject a container that starts successfully but loads the wrong model version?
This is a learning route for practicing the work, not a claim of job readiness. Use the milestone coverage and a reviewer’s feedback to identify what remains to demonstrate. The capability map below separates required route practice from optional extensions. Public checks and self-reported completion are not independent review or hardware qualification.