S L Happy - ML Digest

OpenTelemetry for ML Systems: Practical Observability That Explains What Happened

Imagine debugging a modern ML product without observability. It is like managing an airport where planes keep arriving late, bags […]

Imagine that you need to open $K$ new coffee shops in a city. You want each person to walk to

Think of a machine learning model as a high-performance engine prototype sitting on a pristine workbench. It might run beautifully

Introduction: The Broken Yardstick Imagine you are a textile factory manager in the early 19th century. For years, your entire

There is an old proverb that perfectly captures the intuition behind one of the most fundamental algorithms in machine learning:

1. The Intuition: The Overconfident Weather Forecaster Imagine planning a weekend picnic. You check two weather applications. You cancel the

Fraud detection for payments is a real-time decision problem under uncertainty:you must decide to approve, decline, or step-up a payment

Guardrails are the technical and operational controls that reduce the chance an LLM system causes harm, violates policy, leaks sensitive

1. Problem framing (what “reactive RCA” means) Imagine you are a doctor in a busy emergency room. A patient (the

Imagine you are in a kitchen trying to cook a new, complex dish. You do not just grab random ingredients

Perplexity answers one narrow question: When the true next token is revealed, how confused/uncertain is the model? It is a

BART is a sequence-to-sequence (encoder–decoder) Transformer pretrained as a denoising autoencoder: it learns to reconstruct clean text $x$ from a