Stories by Scarlett Attensil
Best CI/CD Pipelines for Containerized AI Development
Containerized AI applications require sophisticated deployment infrastructure to manage Docker images.
Scarlett Attensil
ML Experiment Tracking: What to Track Across Models, Data, and Production
The vast majority of teams working on large language models (LLMs) and machine learning (ML) systems diligently track hyperparameters.
Scarlett Attensil
Best Practices for Experiment Tracking in MLOps
Machine learning experimentation scales quickly.
Scarlett Attensil
Why AI deployment breaks standard CI/CD
Learn why AI deployment can break standard CI/CD and how runtime controls, shadow testing, rollouts, and rollback reduce risk.
Scarlett Attensil
The Complete AI Experimentation Guide: Test, compare, validate, and ship safely
Artificial intelligence tools aren’t like traditional software.
Scarlett Attensil
MLOps lifecycle: Stages, workflow, and best practices
Understand the MLOps lifecycle from data preparation to monitoring.
Scarlett Attensil
AI pipeline: Preventing drift in production systems
Learn why uncontrolled AI pipeline changes can cause failures in prod.
Scarlett Attensil
LLM observability: Tutorial and best practices
LLM observability analyzes how models behave across development, testing, and production.
Scarlett Attensil
LLM pricing comparison: Tutorial and best practices
Large language models (LLMs) power a wide range of AI applications today.
Scarlett Attensil
