AI400: AI powered Predictive Analytics NEW

Agenda:

• Predictive Analytics Basics & Data Understanding
  • Predictive Analytics overview: problem types & use case categories
  • Statistics and data foundations:
    • distributions
    • correlation vs. causation
    • signal/noise
    • typical pitfalls
  • Data sources & data quality:
    • sensor/time series data
    • event data
    • master data
    • context data
  • Time series fundamentals:
  • sampling
  • missing values
  • outliers
  • trend/seasonality
  • aggregation
  • Hands-on lab: data profiling & preparation of a time series (cleaning, resampling, feature basics)
    
    
• Modeling & Evaluation (what “good prediction” really means)
  • Feature engineering for time series:
    • lags
    • rolling windows
    • calendar features
    • state features
  • Model families:
    • classic ML models for forecasts/risk scores (e.g., gradient boosting/random forest)
    • baselines & simple forecasting models (as a reference)
    • positioning of deep learning for sequences (optional, depending on the group)
  • Setting up evaluation correctly:
    • train/test for time series
    • backtesting
    • avoiding leakage
  • Metrics & interpretation:
    • MAE/RMSE/MAPE
    • precision/recall
    • ROC AUC
  • Hands-on lab: model comparison + backtesting + error analysis
    
    
• From Prediction to Application (Operationalization & Best Practices)
  • From “prediction” to “decision”:
    • thresholds
    • cost/benefit
    • sanity checks
  • Operations & continuous improvement:
    • drift signals
    • monitoring
    • retraining strategy
    • version management
  • Integration into processes:
    • batch vs. near real time
    • interfaces
    • reporting/BI integration
  • Capstone exercise:
    • mini use case as an end-to-end blueprint incl. metrics
    • monitoring idea and next steps

Objectives:

After the AI400 Predictive Maintenance in Industry with Machine Learning course, participants can confidently distinguish predictive analytics problem types (e.g., forecasting vs. risk scoring vs. anomalies), robustly prepare time series and sensor data and make them suitable for modeling, systematically perform feature engineering and model selection while using baselines appropriately, set up evaluation correctly (including backtesting and leakage prevention), and interpret the results in a well-founded way.

Target audience:

The AI400 Predictive Maintenance in Industry with Machine Learning training is intended for:

• Developers, data analysts, data scientists (beginner to intermediate)
• IT professionals/engineers who implement or support predictive analytics use cases
• Subject matter owners with a technical background (e.g., production, maintenance, quality, energy)

Prerequisites:

To follow the learning pace and content of the AI400 Predictive Maintenance in Industry with Machine Learning training, the following prior knowledge is required:

• Fundamentals of Python
• Basic understanding of machine learning (train/test, overfitting, metrics)

Description:

The AI400 AI powered Predictive Analytics course provides a hands-on, end-to-end introduction to predictive analytics with machine learning—from problem formulation through data preparation and modeling to evaluation, operations, and integration into real-world processes. At its core is the question: How can future events, states, or metrics be reliably predicted—and how can this be turned into robust decision support within the organization?

Rather than focusing on a single use case, the AI400 AI powered Predictive Analytics course presents predictive analytics as a reusable pattern across different domains: time series forecasting (e.g., utilization, energy consumption, demand), risk predictions (e.g., probability of failure, quality risk), anomaly detection (e.g., sensor drift, process deviations), and Remaining Useful Life / predictive maintenance as a typical industrial scenario. Participants work with Python on clear, comprehensible examples and build a small end-to-end pipeline—including rigorous evaluation (backtesting, leakage prevention) and best practices for production-oriented use (monitoring, drift, retraining).