DevOps for Data Scientists

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Dan Sullivan

32:16

100 View

01 - Introduction

01 - Welcome.mp4

01:12

02 - Target audience.mp4

00:38

02 - 1. Data Science Development Practices

01 - Data science and software engineering.mp4

01:31

02 - Collecting and munging data.mp4

02:35

03 - Experimenting with data, features, and algorithms.mp4

01:31

04 - Testing and validating models.mp4

01:37

03 - 2. Data Science Models to Production

01 - Version control for data science models.mp4

02:42

02 - Predictive Model Markup Language.mp4

02:07

03 - Deploying models with automation tools.mp4

01:30

04 - 3. Deployment Practices

01 - Deploying to staging environment.mp4

01:09

02 - Canary deployments.mp4

01:15

03 - Securing the data science models in production.mp4

04:30

04 - Monitoring models in production.mp4

02:38

05 - 4. Data Science Models in Containers

01 - Introduction to Docker.mp4

01:38

02 - Creating a Dockerfile for data science models.mp4

02:20

03 - Data science Docker image repository.mp4

01:33

06 - Conclusion

01 - Overview of DevOps best practices for data science.mp4

01:50

Description

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.

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DevOps

Dan Sullivan

Instructor's Courses

Cloud and data architect with extensive experience in data architecture, data science, machine learning, stream processing, and cloud architecture. Capable of starting with vague initiatives and formulating precise objectives, strategies, and implementation plans. Regularly works with C-level and VP executives while also mentoring and coaching software engineers. Adapts well to unforeseen challenges. He is the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer.

Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.