ml-expert

Model deployment with FastAPI involves wrapping your trained PyTorch or scikit-learn model in a REST endpoint. This Skill provides guidance to deploy models as microservices, ensuring scalable inference and proper API integration.

Tracking experiments with MLflow records model parameters, metrics, and artifacts during model training. This Skill demonstrates integrating MLflow to manage your machine learning lifecycle and compare different training runs effectively.

Yes, this Skill covers supervised, unsupervised, and reinforcement learning. It provides expert-level guidance and code examples for model training across these paradigms using scikit-learn and PyTorch.

Data preparation and model training with scikit-learn involve using numpy and pandas to format inputs before fitting a Random Forest classifier. This Skill offers scripts to quickly set up training pipelines and save models using joblib.

Yes, deep learning model training and evaluation are covered using PyTorch. The Skill provides practical examples for building neural networks, training them on your data, and evaluating their performance.

MLOps best practices for model deployment include proper experiment tracking and microservice architecture. This Skill addresses deployment strategies, data preparation, and highlights anti-patterns to avoid during machine learning operations.