How Your ML Model Will Fail - And How to Prepare For It
Explore ML model failure modes, analyze production performance, and set up pragmatic monitoring strategies to prevent degradation and breakage in production.
Minimize Risk and Accelerate MLOps with ML Monitoring and Explainability
Discover strategies to minimize risk and accelerate MLOps through ML monitoring and explainability. Learn to efficiently monitor, troubleshoot, and document ML models for improved performance and compliance.
Real-Time Event Streaming Patterns for AI-Native Applications
Explore how event streaming patterns power AI applications through real-time analytics, data enrichment, and stream processing for enhanced intelligence and reliable infrastructure.
A Guide to Putting Together a Continuous ML Stack
Implement continuous ML training by automating pipelines, detecting performance issues, and triggering model updates with fresh data for improved MLOps maturity.
Obtaining New Insights on Model Behavior with Fiddler - MLOps Meetup #110
Gain insights on model behavior using Fiddler for monitoring, explainability, and analysis. Learn to instrument ML models for observability, register models, and publish events to understand data drift and underperforming cohorts.
Your First Data Science Project: Planning for Success
Learn effective project management techniques for data science, covering agile and waterfall methodologies, autoML, model monitoring, and data drift to ensure successful implementation of data-driven initiatives.