MLOpsPython has comprehensive devops and mlops pipelines that support the full life cycle of machine learning projects. However, it only demonstrates a scikit-learn model with tabular data. Image data typically requires reshaping the images in a separate data processing pipeline and a different type of Azure ML dataset. Computer vision models based on Tensorflow/Keras also requires training and inferencing environments different from scikit-learn.
This item builds and deploys the flower image classification example in the Tensorflow tutorial. Specifically, it modifies MLOps in the following ways -
Has a separate data processing pipeline to reshape the images.
Supports images as Azure ML dataset.
Trains and deploys a Tensorflow/Keras based image classification model.
Preserves the devops and mlops pipelines in MLOpsPython.
MLOpsPython has comprehensive devops and mlops pipelines that support the full life cycle of machine learning projects. However, it only demonstrates a scikit-learn model with tabular data. Image data typically requires reshaping the images in a separate data processing pipeline and a different type of Azure ML dataset. Computer vision models based on Tensorflow/Keras also requires training and inferencing environments different from scikit-learn.
This item builds and deploys the flower image classification example in the Tensorflow tutorial. Specifically, it modifies MLOps in the following ways -