Migrating an Sklearn model training Pipeline to Tensorflow Keras.

Abstraction: Porting sklearn preprocessing pipelines natively into Keras using EasyFlow

Key points:

• Keras preprocessing layers replace sklearn equivalents: Normalization for StandardScaler, IntegerLookup for OneHotEncoder, and StringLookup + IntegerLookup for string categoricals
• The EasyFlow library (easy-tensorflow on PyPI) provides FeatureUnion (equivalent to ColumnTransformer) and FeatureInputLayer as Keras layers, enabling a fully native Keras pipeline
• Preprocessing embedded in the network is saved as part of the model, eliminating a separate sklearn artifact at serving time (enables TensorFlow Serving)
• For training speed, split into a preprocessing_model run on CPU and a training_model run on GPU with prefetched batches; merge them back into a single inference_model before saving
• Author: Fernando Nieuwveldt; EasyFlow repo: github.com/fernandonieuwveldt/easyflow

Connections: Scikit Learn · Tensorflow · Keras · Machine Learning Pipelines · Feature Engineering · Model Serving

Source: https://medium.com/mlearning-ai/migrating-an-sklearn-model-training-pipeline-to-tensorflow-keras-d248754f991d