DS, ML, and AI have moved very far from just running the models only at your local machine. Nowadays models are running in production and helping the business at decision making, which in turn increased the expectations for continuously running the models and making the changes online, but remember running this at a large scale is no easy task.
During this session, we will learn about one such approach with Docker, Kubernetes and Kubeflow which can help us not only in developing but also deploying models at scale, allow us to use distributed setup and Hyperparameter tuning

Machine Learning and AI are changing or would say have changed the way how businesses used to behave. However, the Data Science community is still lacking good practices for organizing their projects and effectively collaborating and experimenting quickly to reduce “time to market”.

During this session, we will learn about one such open-source tool “DVC”
which can help you in helping ML models shareable and reproducible.
It is designed to handle large files, data sets, machine learning models, metrics as well as code