AI TRAINING
Predictive Maintenance Practitioner Bootcamp
Build and deploy machine learning models that predict equipment failures before they happen.
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
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