Machine Learning Engineering
- 5 courses
- 7 hours
On completion of this path, the learner will be able to design and operationalize solutions to maximize the performance and scale of Machine Learning. These solutions will include Machine Learning infrastructure, data and feature quality processes, model formats and training (Real and Batch), in addition to model deployment options - real-time model serving and batch model scoring, monitoring model performance & reliability, scaling Machine Learning models and automated Machine Learning interventions.
Courses in this path
This section is focussed on understanding the requirements of a framework that can cope with Machine Learning applications and their tasks. You will also learn how to evaluate framework on performance, execution, and practical usage requirements.
In this section, you will explore Machine Learning operations with focus on deploying Machine Learning models at scale with the use of Apache Airflow. One will learn to author, schedule, and monitor data pipelines through practical examples using Apache Airflow.
In this section, you will focus on exploring best practices for data quality management for Machine Learning and Machine Learning operations with Kubeflow, Dask and Sagemaker.