Advanced ML: ML Infrastructure
In this advanced-level quest, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on GCP. Machine learning has become easier than ever with the help of the Google cloud platform. Now with the abundance of APIs, any machine learning task can be done with the Google cloud platform.
Machine learning infrastructure includes the resources, processes, and tooling needed to develop, train, and operate machine learning models. It is sometimes referred to as AI infrastructure or a component of MLOps. ML infrastructure supports every stage of machine learning workflows.
Components of a machine learning infrastructure also require solutions for data management, data version control and should provide a ML workbench to give a simple way to train models, work on their research, and optimize models and algorithms.
In one of the labs, we will work with the Scikit-learn model. Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistent interface in Python.