Overview
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Learn the principles of supporting DevOps and how to apply them to data science.
Syllabus
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Introduction
- Welcome
- Target audience
- Data science and software engineering
- Collecting and munging data
- Experimenting with data, features, and algorithms
- Testing and validating models
- Version control for data science models
- Predictive Model Markup Language
- Deploying models with automation tools
- Deploying to staging environment
- Canary deployments
- Securing the data science models in production
- Monitoring models in production
- Introduction to Docker
- Creating a Dockerfile for data science models
- Data science Docker image repository
- Overview of DevOps best practices for data science
