DevOps for Data Scientists: Data Science DevOps
DevOps
| Intermediate
- 16 videos | 1h 12m 26s
- Includes Assessment
- Earns a Badge
In this 16-video course, learners discover the steps involved in applying DevOps to data science, including integration, packings, deployment, monitoring, and logging. You will begin by learning how to install a Cookiecutter project for data science, then look at its structure, and discover how to modify a Cookiecutter project to train and test a model. Examine the steps in the data model lifecycle and the benefits of version control for data science. Explore the tools and approaches to continuous integration for data models, to data and model security for Data DevOps, and the approaches to automated model testing for Data DevOps. Learn about the Data DevOps considerations for data science tools and IDEs (integrated developer environment) and the approaches to monitoring data models and logging for data models. You will examine ways to measure model performance in production and look at data integration with Cookiecutter. Then learn how to implement a data integration task with both Jenkins and Travis CI (continuous integration). The concluding exercise involves implementing a Cookiecutter project.
WHAT YOU WILL LEARN
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Discover the subject areas covered in this courseExamine a cookiecutter project structureModify a cookiecutter project to train and test a modelDescribe the steps in the data model life cycleDescribe the benefits of version control for data scienceDescribe tools and approaches to continuous integration for data modelsDescribe approaches to data and model security for data devopsDescribe approaches to automated model testing for data devops
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Identify data devops considerations for data science tools and idesIdentify approaches to monitoring data modelsDescribe approaches to logging for data modelsIdentify ways to measure model performance in productionAdd directives to the make file to prepare for continuous integrationImplement a data integration task with jenkinsImplement data integration with travis ciIncorporate a model into a cookiecutter project
IN THIS COURSE
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1m 22s
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7m 36sFind out how to examine a cookie cutter project structure. FREE ACCESS
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6m 47sIn this video, you will modify a Cookiecutter project to train and test a machine learning model. FREE ACCESS
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5m 4sUpon completion of this video, you will be able to describe the steps in the data model life cycle. FREE ACCESS
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3m 56sAfter completing this video, you will be able to describe the benefits of version control for data science. FREE ACCESS
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3m 47sUpon completion of this video, you will be able to describe tools and approaches for continuous integration of data models. FREE ACCESS
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5m 3sUpon completion of this video, you will be able to describe approaches to data and model security for Data DevOps. FREE ACCESS
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4m 10sUpon completion of this video, you will be able to describe approaches to automated model testing for Data DevOps. FREE ACCESS
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4m 46sIn this video, find out how to identify Data DevOps considerations for data science tools and IDEs. FREE ACCESS
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3m 4sIn this video, you will identify approaches for monitoring data models. FREE ACCESS
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2m 49sUpon completion of this video, you will be able to describe approaches to logging data models. FREE ACCESS
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4m 33sLearn how to identify ways to measure model performance in production. FREE ACCESS
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5m 22sDuring this video, you will learn how to add directives to the makefile to prepare for continuous integration. FREE ACCESS
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4m 46sIn this video, you will learn how to implement a data integration task with Jenkins. FREE ACCESS
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4m 24sIn this video, you will learn how to implement data integration using Travis CI. FREE ACCESS
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4m 59sIn this video, you will learn how to incorporate a model into a Cookiecutter project. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.