AI/ML model deployment and MLOPS in GCP

This talk will cover deploying ML models and LLMs on Google Cloud Platform (GCP) using MLOps. First, we'll briefly discuss relevant GCP services. Then, we'll explore deployment methods like containerization, batch, and online prediction. We'll also look at key GCP tools such as Vertex AI Model Registry, Model Monitoring, and CI/CD pipelines. A practical demo of deploying an ML model on GCP will be included. We'll also discuss MLOPS specific aspects like Model monitoring, experiment tracking etc

Key Takeaways:

• GCP's Vertex AI streamlines MLOps for ML/LLMs.
• Learn containerized, batch, and online model deployment on GCP.
• Utilize Vertex AI tools: Registry, Monitoring, and CI/CD.
• Practical demo will cover MLOps essentials like monitoring and tracking.

Prerequisites:

• Basic Machine Learning

Srimugunthan is currently working as Data Science Lead in Wells Fargo. Srimugunthan is a data analytics professional with 14+ years of experience in tech and 10+ years of experience in data analytics. His expertise is in Prediction models, Recommender systems, Generative AI, Experimentation, and Causal Inference. He has worked in Finance and consumer internet domains and enjoys solving Data Science problems and identifying new AI use cases to solve business problems.