Phase 1 is the data audit, and it absorbs about 60 percent of the typical project effort. We inventory every data source — historian, SCADA, MES, ERP, manual logbook — assess sampling rates, missingness, label quality, and lineage, then build a feature catalogue that ties every variable to a process meaning. Sixty percent of industrial AI project failures originate in this phase. A model trained on dirty data is not a model; it is a generator of confidently wrong predictions, and most teams discover this only when the model is already on the dashboard.

Phase 2 establishes baselines before sophistication. We start with simple, interpretable methods — rolling averages, isolation forests, statistical process control, ARIMA, exponential smoothing — and only move to deep learning once we can demonstrate the baseline has plateaued. Every fancy model is benchmarked against this baseline using the same hold-out window and the same operating cost assumptions. We have shut down several otherwise-impressive deep learning prototypes because the rolling-window baseline did just as well at a tenth of the operational complexity.

Phase 3 is deployment, and the architecture depends on the use case. Computer vision quality control runs on NVIDIA Jetson or Intel OpenVINO at the line, because line speed plus camera frame rates do not tolerate cloud latency. Predictive maintenance and energy analytics batch-score in the cloud, typically every five to fifteen minutes, against TimescaleDB or InfluxDB. OEE analytics and anomaly detection live in the middle — edge aggregation, cloud reasoning. We deploy via Docker containers, manage with Portainer or lightweight Kubernetes (K3s) at the edge, and version-pin every dependency.

Phase 4 is MLOps, which we treat as non-optional. Every production model is instrumented for drift monitoring against a reference distribution, retrained on a defined cadence or triggered by drift alarms, and A/B-compared against the legacy heuristic it replaced. Model performance, prediction confidence, and downstream business KPI are all logged. When a model degrades — and they all eventually do — the runbook tells the engineer on shift what to do in the next 30 minutes, not the next 30 days.

The supporting stack is deliberately pragmatic: TensorFlow and PyTorch for deep learning, scikit-learn for everything else, MLflow for experiment tracking, InfluxDB and TimescaleDB for time-series storage, Grafana and Power BI for dashboards, FastAPI for inference services. We avoid vendor lock-in where possible — every model can be re-implemented on a different stack with documented effort, because the engineering value sits in the data layer and the features, not in the toolchain.