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LinkedIn Learning

DevOps for Data Scientists

via LinkedIn Learning

Overview

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

Reviews

4.5 rating at LinkedIn Learning based on 112 ratings

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