Location: Colorado Springs, CO
  Remote: Yes
  Willing to relocate: No
  Technologies: Python, Git, AWS, Bash, Docker, PyTorch, YoloV8
  Résumé/CV: https://drive.google.com/file/d/19PTw1oNtkXSV92JhRiOYJvQCnhFFRLEq/view?usp=sharing 
  Email: bradyjohnson84@gmail.com

Dedicated and innovative Machine Learning Engineer with a strong background in machine learning and cloud infrastructure. Proven track record of developing high-impact solutions and optimizing processes in technology-driven environments.

I have used dvc (specifically pipelines and experiments) for a little while now and I have found it to be a great tool for creating a standardized process for training ML models. Workflows in so many different teams consist of a bunch of notebooks that aren't versioned, that are all on developers local machines, and just no reproducibility or standardization. DVC is a great lightweight tool that is easy to setup and use, customizable to whatever hardware or architecture that you are using. Most teams that I have seen have data and models on local machines, and do not version them whatsoever. DVC has been great for creating reproducible models, which has always been the biggest focus point for me. Overall I think it is a great tool and does a whole lot more than just data version control, things like experiments and DVCLive are super great.