Building a machine learning pipeline can be a complex, time-consuming process comprising of several phases. In this session, we share how to leverage Amazon SageMaker platform to tackle the challenges of building a ML pipeline, from data labelling, feature engineering, model training, tuning, to deployment. Using Amazon SageMaker, data scientists and engineers will be able to focus on their core values while Amazon SageMaker does the heavy-lifting of large scale and distributed model training, and infrastructure management.
