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DevOps for Data Scientists

via LinkedIn Learning

Overview

Learn the principles of supporting DevOps and how to apply them to data science.

Data scientists create data models that need to run in production environments. Many DevOps practices are relevant to production-oriented data science applications, but these practices are often overlooked in data science training. In addition, data science and machine learning have distinct requirements, such as the need to revise models while in use. This course was designed for data scientists who need to support their models in production, as well as for DevOps professionals who are tasked with supporting data science and machine learning applications. Learn about key data science development practices, including the testing and validation of data science models. This course also covers how to use the Predictive Model Markup Language (PMML), monitor models in production, work with Docker containers, and more.

Syllabus

Introduction
  • Welcome
  • Target audience
1. Data Science Development Practices
  • Data science and software engineering
  • Collecting and munging data
  • Experimenting with data, features, and algorithms
  • Testing and validating models
2. Data Science Models to Production
  • Version control for data science models
  • Predictive Model Markup Language
  • Deploying models with automation tools
3. Deployment Practices
  • Deploying to staging environment
  • Canary deployments
  • Securing the data science models in production
  • Monitoring models in production
4. Data Science Models in Containers
  • Introduction to Docker
  • Creating a Dockerfile for data science models
  • Data science Docker image repository
Conclusion
  • Overview of DevOps best practices for data science

Taught by

Dan Sullivan

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