Continual Learning and Model Update Strategies for Fine-Tuned Models Training Course
Continual learning encompasses a set of strategies that allow machine learning models to update incrementally and adapt to new data over time.
This instructor-led, live training (online or onsite) is designed for advanced-level AI maintenance engineers and MLOps professionals who wish to implement robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.
By the end of this training, participants will be able to:
- Design and implement continual learning workflows for deployed models.
- Mitigate catastrophic forgetting through proper training and memory management.
- Automate monitoring and update triggers based on model drift or data changes.
- Integrate model update strategies into existing CI/CD and MLOps pipelines.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Continual Learning
- Why continual learning matters
- Challenges in maintaining fine-tuned models
- Key strategies and learning types (online, incremental, transfer)
Data Handling and Streaming Pipelines
- Managing evolving datasets
- Online learning with mini-batches and streaming APIs
- Data labeling and annotation challenges over time
Preventing Catastrophic Forgetting
- Elastic Weight Consolidation (EWC)
- Replay methods and rehearsal strategies
- Regularization and memory-augmented networks
Model Drift and Monitoring
- Detecting data and concept drift
- Metrics for model health and performance decay
- Triggering automated model updates
Automation in Model Updating
- Automated retraining and scheduling strategies
- Integration with CI/CD and MLOps workflows
- Managing update frequency and rollback plans
Continual Learning Frameworks and Tools
- Overview of Avalanche, Hugging Face Datasets, and TorchReplay
- Platform support for continual learning (e.g., MLflow, Kubeflow)
- Scalability and deployment considerations
Real-World Use Cases and Architectures
- Customer behavior prediction with evolving patterns
- Industrial machine monitoring with incremental improvements
- Fraud detection systems under changing threat models
Summary and Next Steps
Requirements
- An understanding of machine learning workflows and neural network architectures
- Experience with model fine-tuning and deployment pipelines
- Familiarity with data versioning and model lifecycle management
Audience
- AI maintenance engineers
- MLOps engineers
- Machine learning practitioners responsible for model lifecycle continuity
Open Training Courses require 5+ participants.
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