Session: MLOps and ModelOps - Why does it matter?
It took a while for the IT industry to realize that Development and deployment goes hand in hand and There could be no successful project without Devops. We should take this learning and adopt MLOPs as a basic element for any AI/ML project.
My talk will consist of with
1: The explaination of development process and introducing devops , and importance of devops
2: Understanding what are the curent trends and best practises in devops.
3: explaining how the development of AI /ML models differs from traditional application develoment
4: How to adopt devops in machine learning annd introducing modelops
5: What is models ops
6: lifecycle of preditive models
7: Best practises Pre and Post model deployment
8: How modelops cover the model lifecycle
- complete understanding of the model lifecycle
- Understanding of Devops and how does regular devops differ from modelOps
- ML model code is only a small part (~5–10%) of a successful ML system, and the objective should be to create value by placing sustainable ML models into production .
An enthusiastic technologist and strategist. Empowering industries by Enterprise Architecture Practices.