Machine Learning Engineering

ML pipelines, feature engineering, model training and evaluation, MLOps, experiment tracking, and model deployment.

Topics

Lessons

CNNs, RNNs, transformers — PyTorch, TensorFlow, JAX

Model versioning, serving, monitoring, retraining — MLflow, Kubeflow, SageMaker

Classification, regression — random forests, SVMs, neural nets

Clustering, dimensionality reduction, autoencoders