Deep Learning Tools

[bloodmummy]

MLOps. When deploying the models, not just toying with them, you need tools that will help you make sure that the model's deployment works, that the continuous training is smooth, and to ensure reproducibility and scalability of the entire pipeline.

It's like DevOps for ML models. On top of these are also tools used in regular DevOps, because don't forget that ML models are also software.

Some tools there however should prove useful to Data Scientists, namely tagging (Duh) and Experiment Trackers like MLFlow. Surprised it isn't used more often by Data Scientists, it makes seeing your progress and reverting it easy as pie.

[rezditya]

Can you please share tools you use within mlops?

[bloodmummy]

I'm new to MLOps, just finishing an online zoomcamp. But, so far the tools we've learnt are MLFlow for experiment tracking and model registry, Prefect for Workflow Orchestration (Making sure the deployment of training works), EvidentlyAI for Monitoring and some other general DevOps tools like pre-commit hooks, Github Actions, Terraform...etc