ML/DL Best Practices: Building Pipelines with Applied Rules

This course examines how to troubleshoot deep learning models and build robust deep learning solutions. In 13 videos learners will explore the technical challenges of managing diversified kinds of data with ML (machine learning) and how to work with its challenges. This course uses case studies to demonstrate the impact of adopting deep learning best practices and how to deploy deep learning solutions in an enterprise. First you will learn the best approach for architecting building and implementing scalable ML services and rules to build ML pipelines into applications. Then learners will examine critical challenges and patterns associated with deploying deep learning solutions in an enterprise. You will learn to use feature engineering to apply rules and features in an application and how to use feature engineering to manage slowed growth training-serving skew optimization refinement and complex models in ML application management. Finally you will examine the checklists that are recommended for project managers to prepare and adopt when implementing machine learning.<