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Course Outline
Introduction to Continual Learning for Government
- Why continual learning matters for government operations
- Challenges in maintaining fine-tuned models for government use cases
- Key strategies and learning types (online, incremental, transfer) for government applications
Data Handling and Streaming Pipelines for Government
- Managing evolving datasets for government agencies
- Online learning with mini-batches and streaming APIs in government environments
- Data labeling and annotation challenges over time for government data sets
Preventing Catastrophic Forgetting in Government Models
- Elastic Weight Consolidation (EWC) for government models
- Replay methods and rehearsal strategies for maintaining model accuracy in government applications
- Regularization and memory-augmented networks to ensure reliable performance for government tasks
Model Drift and Monitoring for Government
- Detecting data and concept drift in government models
- Metrics for assessing model health and performance decay in government systems
- Triggering automated model updates to maintain accuracy for government operations
Automation in Model Updating for Government
- Automated retraining and scheduling strategies for government models
- Integration with CI/CD and MLOps workflows for government agencies
- Managing update frequency and rollback plans to ensure continuity of operations for government services
Continual Learning Frameworks and Tools for Government
- Overview of Avalanche, Hugging Face Datasets, and TorchReplay for government use
- Platform support for continual learning in government (e.g., MLflow, Kubeflow)
- Scalability and deployment considerations for government applications
Real-World Use Cases and Architectures for Government
- Customer behavior prediction with evolving patterns for government services
- Industrial machine monitoring with incremental improvements in government facilities
- Fraud detection systems under changing threat models for government agencies
Summary and Next Steps for Government
Requirements
- An understanding of machine learning workflows and neural network architectures for government applications.
- Experience with model fine-tuning and deployment pipelines in a public sector environment.
- Familiarity with data versioning and model lifecycle management to ensure governance and accountability.
Audience
- AI maintenance engineers for government agencies.
- MLOps engineers responsible for maintaining secure and reliable AI systems in the public sector.
- Machine learning practitioners tasked with ensuring model lifecycle continuity within government operations.
14 Hours
