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Continuous Intelligence - Keeping Your AI Application in Production

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Overview

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This course shows how to adapt continuous delivery practices and tools to keep AI applications running in production as data changes. It covers model packaging, data versioning, evaluation, testing, redeployment, and monitoring, with a workshop demonstrating the continuous delivery cycle.

Syllabus

Introduction
Continuous Delivery for Machine Learning
Build Pipeline
Challenges
Reasons
More types of change
Pipelines
Stages
Technology stack
Demo
Delivery Pipelines
Continuous Intelligence Workshop
How it works
Red pipeline
DBC pull
Redeploy
Continuous Intelligent Cycle
Questions
How to package a model
Bringing data scientists and developers closer
Data versioning
Evaluation stage
Test stage
Supervised learning
Working together
Continuous monitoring
Monitoring

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