AutoML Essentials Training Course

Introduction and Environment Setup for Government

• Overview of AutoML and its significance for government operations
• Configuring Python and R environments for government use
• Setting up remote desktop and cloud environments compliant with federal standards

Exploring AutoML Features for Government

• Core capabilities of AutoML frameworks tailored for government applications
• Advanced hyperparameter optimization and search strategies for enhanced model performance
• Interpreting AutoML outputs and logs to ensure transparency and accountability

How AutoML Selects Algorithms for Government

• Utilization of Gradient Boosting Machines (GBMs), Random Forests, and Generalized Linear Models (GLMs)
• Integration with neural networks and deep learning backends for complex data analysis
• Evaluating trade-offs between accuracy, interpretability, and cost in government contexts

Data Preparation and Preprocessing for Government

• Techniques for handling numeric and categorical data in government datasets
• Strategies for feature engineering and encoding to optimize model performance
• Methods for addressing missing values and data imbalance in public sector data

AutoML for Different Data Types for Government

• Application of AutoML to tabular data using H2O AutoML, auto-sklearn, and TPOT
• Time-series analysis for forecasting and sequential modeling in government scenarios
• Text and NLP tasks such as classification and sentiment analysis for public sector applications
• Image classification and computer vision using Auto-Keras, TensorFlow, and PyTorch for government projects

Model Deployment and Monitoring for Government

• Procedures for exporting and deploying AutoML models in government systems
• Building pipelines for real-time prediction to support decision-making processes
• Strategies for monitoring model drift and retraining to maintain accuracy and relevance

Ensembling and Advanced Topics for Government

• Techniques for stacking and blending AutoML models to enhance predictive capabilities
• Considerations for privacy and compliance in government data usage
• Approaches to cost optimization for large-scale AutoML implementations in the public sector

Troubleshooting and Case Studies for Government

• Common errors and their solutions in government AutoML projects
• Methods for interpreting AutoML model performance to ensure effective outcomes
• Case studies from industry use cases relevant to government operations

Summary and Next Steps for Government