AI TRAINING
Predictive Maintenance Practitioner Bootcamp
Build and deploy machine learning models that predict equipment failures before they happen.
See if this training is the right one for your team — free diagnostic
Run the diagnostic →What it covers
This intensive bootcamp equips reliability engineers and industrial ops leaders with the full stack of predictive maintenance skills: from raw sensor data ingestion and feature engineering to anomaly detection, time-to-failure regression, and production deployment on edge devices or cloud platforms. Participants work through real industrial datasets using Python, scikit-learn, and purpose-built libraries such as tsfresh and ONNX. The programme combines live hands-on labs (60%) with instructor-led concept sessions (40%), culminating in a capstone project where each team delivers a deployable PdM pipeline. Cohorts leave with reusable code templates, a model governance checklist, and a deployment decision framework for edge vs. cloud trade-offs.
What you'll be able to do
- Ingest, align, and engineer features from multi-channel sensor time-series data using Python and tsfresh
- Train, evaluate, and tune anomaly detection models suitable for industrial failure signals
- Build a Remaining Useful Life regression model and interpret its outputs in operational terms
- Make an informed edge-vs-cloud deployment decision and export a trained model to ONNX for edge inference
- Design a retraining and data-drift monitoring strategy to keep PdM models accurate in production
Topics covered
- Sensor data acquisition, cleaning, and time-series alignment
- Feature engineering for temporal industrial data (tsfresh, manual crafting)
- Anomaly detection techniques: Isolation Forest, Autoencoders, statistical control charts
- Remaining Useful Life (RUL) and time-to-failure regression models
- Model evaluation metrics specific to maintenance contexts (false alarm rate, detection lead time)
- Edge vs. cloud deployment trade-offs and ONNX model portability
- MLOps fundamentals for PdM: retraining triggers, data drift monitoring
- OEE (Overall Equipment Effectiveness) impact measurement and ROI framing
Delivery
Delivered over 4-5 consecutive days, either on-site at the client's facility (preferred for access to real sensor data) or remotely via a virtual lab environment with pre-loaded industrial datasets (NASA CMAPSS, PHM Society benchmarks). Participants need a laptop with Python 3.10+ and Docker. Roughly 60% of time is spent in hands-on coding labs; 40% in instructor-led sessions and group design reviews. A shared GitHub repository with starter notebooks is provided. Optional half-day follow-up session available 4 weeks post-bootcamp for deployment troubleshooting.
What makes it work
- Involving both data engineers and maintenance technicians in the bootcamp to close the domain knowledge gap
- Using the organisation's own historical sensor data (even a small subset) for the capstone project
- Establishing a model owner role in operations who monitors alert performance and triggers retraining
- Defining business KPIs (unplanned downtime hours, maintenance cost per unit) before model development begins
Common mistakes
- Training models on clean benchmark data and discovering the approach fails on noisy real plant signals
- Ignoring class imbalance — failures are rare events, so naive accuracy metrics mask poor recall
- Deploying a cloud-only solution on a factory floor with unreliable connectivity, causing critical latency
- Skipping data drift monitoring, so models degrade silently after equipment upgrades or seasonal changes
When NOT to take this
This bootcamp is not the right fit for a team that has no historian or SCADA data pipeline in place — without accessible sensor data, participants cannot complete the capstone and will lack the infrastructure to apply skills post-training.
Providers to consider
Sources
Use cases this training unlocks
- Remaining Useful Life EstimationPredict when critical machinery components will fail to optimize maintenance schedules and reduce downtime.
- IoT-Driven Equipment Failure PredictionPredict machine breakdowns before they happen to eliminate costly unplanned downtime.
- Heavy Equipment Predictive Maintenance MLPredict crane and excavator failures before they happen to cut unplanned downtime on construction sites.
- Vibration Analysis for Rotating EquipmentDetect bearing faults and misalignment in rotating machinery before costly failures occur.
- OEE Predictor for Production LinesPredict Overall Equipment Effectiveness and pinpoint the root causes of availability, performance, and quality losses.
- AI-Enhanced Statistical Process ControlDetect subtle process drifts and predict quality deviations before defective parts are produced.
Other trainings at this level
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.