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Machine Learning Fundamentals: a/b testing with python
- A/B testing with Python is a critical aspect of modern machine learning systems ensuring robustness and scalability.
- It involves controlled testing of multiple model versions in production environments with traffic allocation based on predefined rules.
- Various use cases include fraud detection, recommendation engines, medical diagnosis, autonomous driving, and search ranking.
- The architecture involves features like data sources, a feature store, traffic splitter, model versions, prediction service, and monitoring.
- Implementation strategies include Python orchestration for traffic routing and Kubernetes deployment for traffic splitting.
- Failure modes encompass stale models, feature skew, latency spikes, data corruption, and traffic routing errors.
- Performance tuning techniques focus on metrics like latency, throughput, accuracy, and cost optimization.
- Monitoring and observability tools like Prometheus, Grafana, and Datadog are essential for tracking critical metrics.
- Security, policy, and compliance aspects emphasize adherence to regulations, audit logging, secure data access, and governance tools.
- CI/CD integration automates A/B testing processes, enforces quality checks, and supports automated rollback strategies.
- Common engineering pitfalls include ignoring feature skew, lack of monitoring, complex traffic routing, and insufficient automated rollback mechanisms.
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