The last mile problem in ML
“We have built a machine learning model, What next?”
There is quite a bit of journey that one needs to cover from building a model in Jupyter notebook to taking it to production.
I would like to call it as the “last mile problem in ML” , this last mile could be a simple tread if we embrace some good ideas.
This talk covers some of these opinionated ideas on how we can get around some of the pitfalls in deployment of ML models in production.
We would go over the below questions in detail think about solutions for them.
- How to fix the zombie models apocalypse, a state when nobody knows how the model was trained ?
- In Science, experiments are found to be valid only if they are reproducible. Should this be the case in Datascience as well ?
- Training the model in your local machine and waiting for an eternity to complete is no fun. What are some better ways of doing this ?
- How do you package your machine learning code in a robust manner?
- Does an ML project have the luxury of not following good Software Engineering principles?
- Discussion on some of the issues with deploying ML models to production.
- Discussion about
mlflowincluding a quick demo.
- Discussion about
sagemakerBYO algorithms training.
- Discussion about packagining ML code in a robust manner.
- Highlevel understanding of machine learning.
My name is Krishna Sangeeth. I am currently working as a DataScientist @ Ericsson Global AI Accelerator (GAIA) . Prior to Ericsson, I was working @ Indix as an ML Engineer. I am a passionate programmer always on the look out for learning something new. I am an opensource enthusiast and have been able to make successful contributions to some of my favorite projects such as scikit-learn , mlflow, sagemaker etc.
Github : @whiletruelearn
Twitter : @whiletruelearn
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