Build production-ready anomaly detection systems using deep learning for observability data
This section covers advanced techniques for applying machine learning to observability data, with a focus on building production-grade anomaly detection systems using ResNet embeddings and self-supervised learning.
What’s Here¶
These tutorials guide you through building a complete anomaly detection system from scratch:
Topic | What You’ll Learn |
|---|---|
ResNet Architecture | Adapting residual networks for tabular observability data — Understanding how to build custom TabularResNet models for time-series and structured data |
Self-Supervised Learning | Training without labels using contrastive learning — Learn techniques to train embeddings on unlabeled observability data |
Production Deployment | FastAPI services and vector databases — Deploy real-time inference systems with monitoring and automated retraining |
Learning Approach¶
Production-First: Every example is designed to be deployable in production environments, not just toy demonstrations.
Hands-On Code: All tutorials are executable Jupyter notebooks with sample data—run them, modify them, build your own systems!
Complete MLOps: Learn the full lifecycle from data preparation through deployment, monitoring, and retraining.
Full Tutorial Series¶
For the complete, comprehensive guide to building anomaly detection systems with ResNet embeddings:
Observability Anomaly Detection →
The full tutorial series includes:
ResNet Architecture: Understanding residual networks and adapting them for tabular data
Feature Engineering: Transforming OCSF observability data into model-ready features
Self-Supervised Learning: Training on unlabeled data using contrastive learning
Embedding Quality: Evaluating embeddings with quantitative and qualitative methods
Anomaly Detection: Applying distance-based, density-based, and ensemble methods
Production Deployment: FastAPI services, vector databases, and real-time inference
MLOps: Monitoring, drift detection, and automated retraining
Multi-Source Correlation: Root cause analysis across logs, metrics, and traces
What You’ll Build¶
A complete production system including:
Custom TabularResNet model trained with self-supervised learning
Embedding service (FastAPI) for real-time inference
Vector database for fast k-NN similarity search
Anomaly detection through vector operations
Monitoring and automated retraining pipeline
Why Observability + ML Matters¶
Understanding how to apply ML to observability data is critical for:
Security Operations: Detecting anomalous behavior and potential threats
System Reliability: Identifying performance degradation before it impacts users
Root Cause Analysis: Correlating events across distributed systems
Proactive Monitoring: Moving from reactive alerts to predictive insights
Scale: Handling massive observability data volumes efficiently
Prerequisites¶
These tutorials assume:
Basic Python and PyTorch knowledge
Understanding of neural networks (or see our Neural Networks From Scratch series)
Familiarity with REST APIs and deployment concepts helpful but not required
Target Audience¶
ML engineers building anomaly detection systems
Security engineers working with observability data
Data scientists interested in self-supervised learning
Anyone applying deep learning to tabular/observability data
Where to Start¶
New to neural networks? Start with our Neural Networks From Scratch series to build foundations.
Ready to build? Dive into the full tutorial series and start building your anomaly detection system.
These tutorials use production-grade tools and techniques used by leading tech companies to monitor and secure their infrastructure at scale.