Evaluation Metrics

The Complete Decision Framework for ML Model Deployment: Edge, Cloud, Custom Models, and Model as a Service (MaaS)

A model can be accurate in a demo and still fail in the product. It may respond too slowly for […]

The question “Which frontier LLM is best?” has become less useful than it sounds. A model can lead a broad

Retrieval-augmented generation (RAG) promises answers grounded in an external knowledge base. In practice, an answer can be wrong because the

Imagine you are hiring a new employee who will have access to your email, your calendar, your code repositories, and

Imagine that two doctors examine the same patient and both say, “The treatment worked.” One doctor means the fever dropped.

Imagine a model that predicts loan defaults. During training, the pipeline pulls account_balance from the warehouse today, but the examples

Imagine hiring a new teammate who can search the web, write code, call APIs, and make decisions without asking for

ONNX (Open Neural Network Exchange) is a standard, open-source format for representing machine learning models as a computation graph. In

This article is a system-design blueprint for a YouTube-like recommender: how it serves results in milliseconds, how it learns from

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

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

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