Discover the crucial element missing from production machine learning stacks in this 34-minute conference talk from MLOps World: Machine Learning in Production. Explore how AI observability can bridge the gap between AI and human operators, addressing operational challenges that arise after ML application deployment. Learn about a powerful new logging library designed specifically for AI applications, enabling testing, monitoring, and debugging of both AI systems and upstream data pipelines. Gain insights into handling large-scale data, optimizing memory usage, and producing statistically accurate log profiles for structured and unstructured data. Walk away with best practices to enhance MLOps in your team and understand the importance of robust logging tools in refining ML operations.
The Critical Missing Component in the Production ML Stack - Data Logging for AI Applications
MLOps World: Machine Learning in Production via YouTube
Overview
Syllabus
The Critical Missing Component in the Production ML Stack
Taught by
MLOps World: Machine Learning in Production
Related Courses
-
The Critical Missing Component in the Production ML Stack - Data Logging for AI Applications
-
Machine Learning Engineering for Production (MLOps)
-
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
-
MLOps Bootcamp: Mastering AI Operations for Success - AIOps
-
MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning
-
Smart Analytics, Machine Learning, and AI on Google Cloud
