Deploying ML Models & LLMs on GCP with MLOps: A Practical Demo

Description

In this comprehensive guide, we'll walk you through the process of deploying Machine Learning (ML) models and Large Language Models (LLMs) on the Google Cloud Platform (GCP) using MLOps best practices. First, we'll provide a concise overview of the key GCP services relevant to MLOps. Then, we'll dive deep into various deployment strategies, including containerization, batch prediction, and online prediction. You'll get a close look at essential GCP tools that streamline the MLOps lifecycle, such as the Vertex AI Model Registry for versioning and managing your models, Model Monitoring for detecting drift and ensuring performance, and CI/CD pipelines for automating your ML workflows. The highlight of this session is a practical, hands-on demo where we deploy an ML model on GCP from start to finish. We'll also cover crucial MLOps aspects like experiment tracking to log and compare your model runs and model monitoring to maintain your model's accuracy and reliability in production. Key Takeaways: - Understand how GCP's Vertex AI simplifies and accelerates MLOps for both traditional ML models and LLMs. - Learn the ins and outs of containerized, batch, and online model deployment on the Google Cloud Platform. - Discover how to effectively utilize powerful Vertex AI tools, including the Model Registry, Model Monitoring, and CI/CD pipelines. - Gain practical experience through a real-world demo that covers essential MLOps practices like model monitoring and experiment tracking. Prerequisites: A basic understanding of Machine Learning concepts.