Agentic Design

Patterns
🌊

Online Learning for Agents(OLA)

Continuous learning from streaming data for real-time adaptation in dynamic environments

Complexity: highLearning and Adaptation

🎯 30-Second Overview

Pattern: Continuously adapt models by learning incrementally from streaming data in real-time

Why: Enables adaptation to changing environments, concept drift, and evolving patterns without expensive retraining

Key Insight: Sequential learning with bounded regret allows models to stay current while maintaining computational efficiency

⚡ Quick Implementation

1Initialize:Set up model with incremental learning capability
2Stream:Process data samples sequentially as they arrive
3Update:Adapt model parameters with each new example
4Regularize:Apply constraints to prevent catastrophic forgetting
5Monitor:Track performance and adapt learning rates dynamically
Example: model + streaming_data → incremental_updates → continuously_adapted_model

📋 Do's & Don'ts

✅Implement adaptive learning rates based on data characteristics
✅Use memory replay or rehearsal buffers for important samples
✅Apply regularization techniques to prevent catastrophic forgetting
✅Monitor drift detection and concept change indicators
✅Implement efficient incremental algorithms (SGD, online gradient)
✅Use sliding window approaches for recent data emphasis
❌Update too aggressively without considering stability
❌Ignore concept drift and distribution changes
❌Use fixed learning rates for all data types
❌Apply without proper memory management strategies
❌Neglect computational and latency constraints

🚦 When to Use

Use When

  • • Data arrives continuously in streaming fashion
  • • Distribution changes over time (concept drift)
  • • Memory and computational resources are limited
  • • Real-time adaptation is critical for performance
  • • Batch retraining is too expensive or slow

Avoid When

  • • Data is available in complete batches
  • • Distribution is stable and stationary
  • • High accuracy requires extensive training
  • • Computational resources are abundant
  • • Offline training meets all requirements

📊 Key Metrics

Regret Bound
Cumulative loss vs optimal offline algorithm
Adaptation Speed
Time to recover from concept drift
Memory Efficiency
Storage requirements vs batch methods
Computational Cost
Processing time per update
Forgetting Rate
Knowledge retention over time
Drift Detection Accuracy
Precision/recall for concept changes

💡 Top Use Cases

Real-time Recommendation Systems: Adapt to changing user preferences and behavior patterns
Financial Trading: Learn from market dynamics and adapt to regime changes
Fraud Detection: Continuously adapt to new fraud patterns and techniques
IoT Sensor Networks: Adapt models to changing environmental conditions
Content Personalization: Real-time adaptation to user engagement and preferences
Autonomous Systems: Continuous learning from environmental interactions

References & Further Reading

Deepen your understanding with these curated resources

Foundational Online Learning

Online Learning and Online Convex Optimization (Shalev-Shwartz, 2012)

Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (Bubeck & Cesa-Bianchi, 2012)

Online Learning: A Comprehensive Survey (Hoi et al., 2021)

Introduction to Online Optimization (Hazan, 2016)

Incremental Learning Algorithms

Stochastic Gradient Descent and Online Learning (Bottou, 2010)

Adaptive Subgradient Methods for Online Learning (Duchi et al., 2011)

Adam: A Method for Stochastic Optimization (Kingma & Ba, 2014)

Online Learning with Kernels (Kivinen et al., 2004)

Concept Drift & Adaptation

Learning under Concept Drift: A Review (Gama et al., 2014)

A Survey on Concept Drift Adaptation (Lu et al., 2018)

Adaptive Learning from Evolving Data Streams (Bifet & Gavalda, 2007)

ADWIN: Adaptive Windowing for Mining Changing Data Streams (Bifet & Gavalda, 2007)

Continual Learning Methods

Elastic Weight Consolidation: Overcoming Catastrophic Forgetting (Kirkpatrick et al., 2017)

Gradient Episodic Memory for Continual Learning (Lopez-Paz & Ranzato, 2017)

PackNet: Adding Multiple Tasks to a Single Network (Mallya & Lazebnik, 2018)

Progressive Neural Networks (Rusu et al., 2016)

Memory-Based Online Learning

Experience Replay in Online Learning (Rolnick et al., 2019)

Memory-Efficient Experience Replay (Isele & Cosgun, 2018)

Reservoir Sampling for Online Learning (Vitter, 1985)

Online Learning with Memory Networks (Santoro et al., 2016)

Recent Advances (2023-2024)

Online Continual Learning with Natural Distribution Shifts (Cai et al., 2023)

Adaptive Online Learning with Gradient Compression (Liu et al., 2023)

Meta-Learning for Fast Adaptation in Online Settings (Wang et al., 2024)

Efficient Online Learning with Memory Constraints (Chen et al., 2024)

Streaming Data Processing

Apache Kafka Streams: Online Learning Integration

Apache Flink: Stream Processing for ML

River: Online Machine Learning Library

Vowpal Wabbit: Fast Online Learning

Multi-Armed Bandits

A Contextual-Bandit Approach to Personalized News Article Recommendation (Li et al., 2010)

Thompson Sampling for Contextual Bandits (Agrawal & Goyal, 2013)

LinUCB Disjoint: A Linear Upper Confidence Bound Algorithm (Li et al., 2010)

Neural Contextual Bandits with UCB-based Exploration (Zhou et al., 2020)

Implementation Frameworks

scikit-multiflow: Multi-output Stream Learning

River: Online Machine Learning in Python

MOA (Massive Online Analysis): Stream Mining

Avalanche: Continual Learning Library

Production Systems

TensorFlow Extended (TFX): Online Learning Pipelines

Kubeflow: Online ML on Kubernetes

MLflow: Online Model Management

Apache Beam: Stream Processing for ML

Evaluation & Benchmarks

CLAD: Continual Learning Assessment Dataset

CORe50: Continual Object Recognition Benchmark

CLEAR: Continual Learning Benchmark

OpenML: Online Learning Datasets

Industry Applications

Netflix: Online Learning for Recommendations

Uber: Real-time ML for Dynamic Pricing

LinkedIn: Online Learning for Feed Ranking

Google: Online Learning at Scale

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