Simplify Experimentation, Deployment and Collaboration for ML and AI Models
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
Outline/Structure of the Demonstration
5 Minutes - Why experimentation, Deployment, and reproducibility are need of hour and define 'DVC'
5 Minutes - Setup, Features and WorkFlow of DVC tool
10 Minutes - What all are the Use Cases followed by Demo
People will be able to learn how to leverage the software engineering principles we learned and how to apply it into the Data Science world and how we can simplify the workflow so that ML and AI models can run at large scale
Data Scientist, Data Engineers, Data Managers
Prerequisites for Attendees
On the concept side they should be knowing how the model development, deployment workflow works, its challenges.
On the tooling side they should be knowing Python, Git etc.
schedule Submitted 1 year ago
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