Unit 10: Machine Learning Operations

Machine Learning Operations (MLOps) are the approaches, technologies, and tools that are used to automate and streamline the lifecycle of machine learning models, starting from training and ending with deployment in production.

In this unit we introduce the field of Machine Learning (ML) and develop the first set of techniques for supervised learning. We will explore key ML concepts including linear regression and supervised classification, with a focus on life science applications. We also introduce the field of Deep Learning (DL). We will gain hands on experience with key DL concepts, including activation functions and neural networks. Finally, will also explore how to find and use pre-trained models for inference, and how to share the models we have trained with others.

• Jupyter Quickstart
• Introduction to Machine Learning
• Linear Regression
• Supervised Classification
• Intro to Deep Learning
• Build Your Own Neural Network
• From Training to Production
• Data Standardization And Pipelines

Note: This content is adapted from COE 379L: Software Design For Responsible Intelligent Systems and TACC’s Machine Learning for Life Sciences Research Workshop Please refer to those materials for further information.