Guides
Technical guides
for builders.
Practical notes on machine learning, LLMs, and real-world deployment. From a first experiment to a working system.
Choosing the right embedding model
A practical guide to embedding models, including trade-offs, evaluation methods, and retrieval quality.
Machine Learning6 min read
Deploy your first ML API with FastAPI
Turn a model into a production-ready API with validation, testing, and containerization.
MLOps7 min read
What model accuracy doesn’t tell you
Go beyond a single number. Explore evaluation metrics, test sets, and practical failure analysis.
Machine Learning6 min read
Fine-tuning vs. prompting: where to start
Understand the trade-offs between prompting and fine-tuning, and when to use each approach.
Generative AI5 min read
A cleaner Python project structure
A maintainable project structure for machine learning, with packaging, configuration, and testing.
Python4 min read
Monitoring models after deployment
Practical strategies for monitoring performance, detecting drift, and setting useful alerts.
MLOps8 min read