Continual Learning and Model Update Strategies for Fine-Tuned Models Training Course
Continuous learning encompasses a range of strategies that allow machine learning models to update incrementally and adapt to new data as it arrives over time.
This instructor-led, live training (available online or onsite) is designed for advanced AI maintenance engineers and MLOps professionals who want to establish robust continuous learning pipelines and effective update strategies for deployed, fine-tuned models.
Upon completion of this training, participants will be capable of:
- Designing and implementing continuous learning workflows for deployed models.
- Reducing catastrophic forgetting through appropriate training techniques and memory management.
- Automating monitoring and update triggers in response to model drift or data shifts.
- Integrating model update strategies into existing CI/CD and MLOps pipelines.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To arrange customized training for this course, please contact us.
Course Outline
Introduction to Continuous Learning
- The importance of continuous learning.
- Challenges associated with maintaining fine-tuned models.
- Key strategies and types of learning (online, incremental, transfer).
Data Handling and Streaming Pipelines
- Managing evolving datasets.
- Online learning using mini-batches and streaming APIs.
- Addressing challenges in data labeling and annotation 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 assessing model health and performance decay.
- Triggering automated model updates.
Automation in Model Updating
- Strategies for automated retraining and scheduling.
- Integration with CI/CD and MLOps workflows.
- Managing update frequency and rollback plans.
Continuous Learning Frameworks and Tools
- Overview of Avalanche, Hugging Face Datasets, and TorchReplay.
- Platform support for continuous learning (e.g., MLflow, Kubeflow).
- Considerations for scalability and deployment.
Real-World Use Cases and Architectures
- Predicting customer behavior with evolving patterns.
- Industrial machine monitoring with incremental improvements.
- Fraud detection systems adapting to changing threat models.
Summary and Next Steps
Requirements
- A solid 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.
Target Audience
- AI maintenance engineers.
- MLOps engineers.
- Machine learning practitioners responsible for ensuring continuity in the model lifecycle.
Open Training Courses require 5+ participants.
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