Learn more about the joint AWS & Iguazio solution: https://www.iguazio.com/partners/aws/
Start working with MLRun, the open-source MLOps orchestration framework: https://github.com/mlrun/mlrun
We’ve expanded our SCM integrations to include GitLab, BitBucket Cloud, and BitBucket Server so you can implement best practices for machine learning across the lifecycle.
The breakdown:
00:00 - Intro
02:15 - MLOps Overview
05:03 - Feature Engineering
07:44 - MLOps Workflow
10:44 - Solution: Feature Store
14:25 - Feature Store Competitive Landscape
17:03 - Features of a Feature Store
21:01 - CTO: Feature Store Sneakpeak
25:55 - Python Code example
27:57 - ML Pipeline example
30:07 - Covid-19 Patient Deterioration
33:26 - LIVE DEMO
52:45 - QA
Machine learning models are often used for decision support—what products to recommend next, when an equipment is due for maintenance, and even predict whether a patient is at risk. The question is, do organizations know how these models arrive at their predictions and outcomes? As the application of ML becomes more widespread, there are instances where an answer to this question becomes essential. This is called model explainability.
