Intel MLOps Professional study plan

Map the end-to-end MLOps lifecycle and write down which stage each Intel toolkit (oneAPI AI Analytics Toolkit, OpenVINO, Intel Extension for PyTorch, Modin, scikit-learn-intelex) targets

Draft a solution-scoping checklist covering business KPI, latency/throughput targets, data sensitivity, hardware target (CPU, iGPU, discrete GPU, NPU), and success criteria

Install the oneAPI AI Analytics Toolkit base environment and verify conda/apt packages resolve on your workstation

Benchmark a stock PyTorch or scikit-learn training script on your CPU to establish a baseline you will reuse all plan

Convert the reference model to OpenVINO IR and compare FP32 CPU inference latency against the native framework baseline

Apply Post-Training Quantization with the NNCF/OpenVINO POT-style workflow to INT8 and measure accuracy delta against a held-out set

Instrument the training script with experiment tracking (MLflow or TensorBoard) capturing hyperparameters, metrics, and artifact hashes

Document quantization calibration-set size and its effect on accuracy so you can defend the tradeoff

Build a reproducible feature pipeline using a dataframe engine (Modin/pandas) with explicit schema validation and null handling

Implement a feature store or versioned feature table with point-in-time correctness for training vs serving

Add data drift baseline statistics (distribution summaries per feature) to the pipeline output

Configure an experiment-tracking comparison across at least three feature-set variants

Automate data validation gates that fail the pipeline on schema or distribution violations

Run a hyperparameter sweep with tracked runs and select a model using a documented, pre-registered metric

Register the winning model in a model registry with lineage back to data version and code commit

Reproduce a prior run from the registry to prove lineage is complete

Package the optimized model behind an inference server (OpenVINO Model Server or a FastAPI/Triton-style wrapper) with a health endpoint

Containerize the service and set CPU/thread affinity and batch-size knobs, then measure p50/p95 latency under load

Add a canary or blue-green rollout path with an automated rollback trigger

Write a load-test script that reproduces your latency numbers on demand

Instrument the service with request, prediction, and feature-distribution metrics exported to a monitoring backend

Implement drift detection that compares live feature distributions to the stored baseline and alerts above a threshold

Define model-quality monitoring (delayed-label accuracy or proxy metric) and a retraining trigger policy

Simulate a drift event and confirm the alert fires and the retraining trigger is logged

Build a CI pipeline that lints, tests, retrains, re-quantizes, and validates accuracy gates on every merge

Add a CD stage that promotes a model version only after gates pass and records an approval step

Codify governance artifacts: model card, intended-use statement, and an audit trail of promotions

Document the decision log for at least two scoping/tooling tradeoffs made during the plan

Threat-model the serving stack and implement at least two controls (authn/authz on the endpoint, secrets management, or input validation)

Apply a privacy technique appropriate to your data (PII minimization, aggregation, or differential-privacy noise) and document the impact

Run a fairness or bias check across sensitive subgroups and record findings and mitigations

Complete two timed mock exams and log every missed item by domain to target final review