This course offers a thorough examination of Machine Learning Operations (MLOps) as they relate to computer vision. The course starts with an overview of MLOps and computer vision, then delves deeply into the fundamentals of machine learning and particular computer vision approaches. 

Attendants will get knowledge of the major MLOps platforms and technologies as well as appropriate data management techniques. The model development lifecycle is also covered in the course, with an emphasis on hyperparameter optimization and model validation. By integrating MLOps into computer vision research, researchers can ensure that their models are not only theoretically sound but also practically viable, capable of being deployed and maintained in real-world environments.

A case study on the use of MLOps in medical imaging offers helpful advice. An examination of upcoming MLOps trends and problems brings the course to a close. The course is intended for those interested in the operational elements of machine learning models, particularly in the context of computer vision. It is necessary to have some prior programming expertise, and a fundamental understanding of machine learning techniques.

Target Audience

Computer Science Students: Undergraduate or graduate students interested in concentrating in machine learning operations and computer vision who are studying computer science, data science, or a similar discipline.

Machine Learning Practitioners: Professionals in the machine learning industry who wish to improve their knowledge of and abilities in MLOps and its use in computer vision.

Data Scientists: Data scientists who want to comprehend the practical facets of implementing machine learning models, particularly in the context of computer vision.
Software Engineers: Software engineers who wish to comprehend the lifespan of machine learning models, from development to deployment and maintenance, and who are making the transfer into data-centric roles.

Medical Professionals: Healthcare industry experts that are curious about how MLOps might be used in medical imaging and other computer vision applications in the field.

Pre-requisites

This tutorial was recently conducted at the Mexican Congress of Artificial Intelligence 2023 (COMIA). We had approximately 20 attendees from various universities, the majority of whom were PhD students.
•             Github Repo: https://github.com/Ivanrs297/patrones-mlops-comia
•             Tutorial Certficate: https://photos.app.goo.gl/jZJxK3JCtqicYwL6A

Technical requirements

•             Lakshmanan, V., Robinson, S., & Munn, M. (2020). Machine learning design patterns. O'Reilly Media.
•             Made With ML https://madewithml.com/ 
•             MLOps Papers https://github.com/visenger/awesome-mlops/blob/master/papers.md 
•             MicrosoK MLOps Examples https://github.com/microsoft/MLOps 
•             A curated list of references for MLOps https://github.com/visenger/awesome-mlops 
•             How to avoid machine learning pitfalls: a guide for academic researchers: https://arxiv.org/abs/2108.02497 
•             Hyperparameter Tunning by Google: https://github.com/google-research/tuning_playbook