Deploying Machine Learning Solutions
Deploy models with Flask, on serverless, on Google AI Platform and to AWS SageMaker.
Model development does not end at deployment. Active maintenance is an integral part of the ML model lifecycle.
The traditional ML workflow follows a linear sequence from data collection to deployment. However, this sequence is cyclical in practice: deployment inevitably triggers a new retraining cycle.
This lifecycle illustrates that deployment is not an endpoint but a step in a continuous model improvement process.
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What's inside
7 sections- 1 Table of Contents
- 2 Module 1 – Understanding Factors That Impact Deployed Models
- 3 Module 2 – Deploying ML Models with Flask
- 4 Module 3 – Deploying to Serverless Environments
- 5 Module 4 – Deploying to Google AI Platform
- 6 Module 5 – Deploying Deep Learning Models to AWS SageMaker
- 7 Comparative Summary Table
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