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.


.. toctree::
   :maxdepth: 1

   jupyter_quickstart
   intro_to_ml
   linear_regression
   supervised_classification
   intro_to_dl
   build_your_own_nn
   model_to_production
   data_standardization_and_pipelines



Note: This content is adapted from
`COE 379L: Software Design For Responsible Intelligent Systems <https://coe-379l-sp24.readthedocs.io/en/latest/index.html>`_
and
`TACC's Machine Learning for Life Sciences Research Workshop <https://life-sciences-ml-at-tacc.readthedocs.io/en/latest/>`_
Please refer to those materials for further information.