Learners will discover the infrastructure, frameworks, and tools that can be used to build data pipelines and visualization for machine learning (ML) in this 10-video course exploring end-to-end approaches for building and deploying ML applications. You will begin with a look at approaches to identifying the right infrastructure for data and ML, and building data pipelines for ML deployments. Examine the iterative process in building ML models with Machine Learning Studio; implement machine learning visualization, and classify frameworks and tools for ML. Next, observe how to build generalized low-rank models by using H2O and integrate them into a data science pipeline to make better predictions. Explore the role of model metadata in applying governance in ML, and also ML risk mitigation—recognizing how ML risk analysis and management approaches can be used to mitigate risks effectively. In the exercise you will recall learning build and deployment frameworks, use Python to implement visualization for ML, and build a simple ML model by using Microsoft Azure Machine Learning Studio.