English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 0h 32m | 53 MB
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.
Topics include:
- Using Git for version control
- Incorporating model testing into the deployment process
- Working with the Predictive Model Markup Language
- Securing the data science models in production
- Monitoring models in production
- Creating a Dockerfile for data science models
Table of Contents
Introduction
1 Welcome
2 Target audience
Data Science Development Practices
3 Data science and software engineering
4 Collecting and munging data
5 Experimenting with data features and algorithms
6 Testing and validating models
Data Science Models to Production
7 Version control for data science models
8 Predictive Model Markup Language
9 Deploying models with automation tools
Deployment Practices
10 Deploying to staging environment
11 Canary deployments
12 Securing the data science models in production
13 Monitoring models in production
Data Science Models in Containers
14 Introduction to Docker
15 Creating a Dockerfile for data science models
16 Data science Docker image repository
Conclusion
17 Overview of DevOps best practices for data science
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