Description:

Description:

MLOps is still a process companies are adjusting to. Building Machine Learning models is becoming simpler and efficient day by day, but deploying them is another story altogether. This talk aims to provide a foot in the door for aspiring MLOps Engineers by providing them an understanding of how to deploy models in production. This will also help them guide Data Scientists into making their experiments more organized with better tracking and painless processes.

Outline of the talk (Tentative):

• Introduction of ML Lifecycle (2 mins)

• Introduction to MLFlow (2 mins)

• Experiments and Runs (2 mins)

• MLFlow Components (9 mins)

• Short Demo of an MLFlow project (10 mins)

• Q/A (5 mins)

Takeaways:

• Have a basic understanding of the MLFlow tool

• Understand of how to make components modular via packaging

• Insights about using Model Registry

• Guide Data Scientists to make their experiments organized with easy-to-use processes

Prerequisites:

Should have basic knowledge of Machine Learning and/or Machine Learning Lifecycle

Speaker Info:

Sakshi Jain is currently working as an MLOps Engineer at Schneider Electric in Bangalore, India. She started as a Data Scientist and transitioned into an MLOps Engineer. Her expertise is in building custom and scalable solutions for real-world business problems for various Machine Learning use cases, combined with her experience in building models with Supervised and Unsupervised learning, Computer Vision, and Reinforcement Learning she has a unique perspective of the challenges faced by both Data Scientists and MLOps Engineers.

Speaker Links:

https://www.linkedin.com/in/sakshi-jain-india