Highlight of the Week - Continuously Deployed Machine Learning
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How to structure and serialize sklearn code for model deployment
How to create API endpoints for machine learning code
How to continuously deploy changes to modeling code
Introduction
Who am I, and who are you?
The problem with Jupyter
The Data Hierarchy of Needs
Model deployment overview
Model Preparation ( 20 | 30 minutes )
Model Scaffolding
ML and pipeline objects
Model serialization options
Exercise: Serialize a machine learning model
API Development ( 30 | 70 minutes )
FastAPI Overview
FastAPI for Flask Users
FastAPI and ML models
Exercise: Connect a machine learning model to an API endpoint
Deploying to Heroku ( 20 | 95 minutes )
Setup and configure Heroku
Connect a repo to Heroku
Deploy changes to Heroku
Exercise: Deploy a API endpoint to Heroku
Deploying to Dokku ( 25 | 110 Minutes )
Benefits of Dokku
Server setup and configuration
Dokku configuration and deployment
Connect a custom domain to Dokku
Exercise: Deploy an ML-enabled endpoint to Dokku
Conclusion ( 5 | 115 minutes )
Data Scientists/Engineers who work with Jupyter notebooks
Engineers that deploy machine learning APIs
Experience with Python, pandas and scikit-learn
Experience with Flask will be helpful, but is not required