DevOps for Data Scientists: Deploying Data DevOps
DevOps
| Intermediate
- 8 Videos | 33m 28s
- Includes Assessment
- Earns a Badge
In this course, learners will explore deploying data models into production through serialization, packaging, deployment, and rollback. You will begin by watching how to serialize models using Python and Pandas. Then the 8-video course takes a look at the tools and approaches to model packaging and deployment. Next, you will explore the concept of the blue-green deployment strategy for data DevOps, which is the strategy for upgrading running software. This leads into examining the concepts behind the Canary deployment strategy in terms of data DevOps. Canary deployments can be regarded as a phase or test rollout on updates and new features. Then take a look at versioning and approaches to rolling back models for machine learning with DevOps. Finally, you will learn about some of the considerations for deploying models to web APIs (application programming interfaces). The concluding exercise involves creating a model by using Python and Pandas, then serializing the results of the model to a file.
WHAT YOU WILL LEARN
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serialize models using Python and pickledescribe tools and approaches to model packaging and deploymentdescribe the blue-green deployment strategy for Data DevOpsdescribe the canary deployment strategy for Data DevOps
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describe approaches to rolling back model versionsexplore approaches to deploying models to web APIsuse python and pandas to serialize a model
IN THIS COURSE
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1.Course Overview1m 16sUP NEXT
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2.Model Serialization5m 7s
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3.Model Packaging and Deployment4m 10s
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4.Blue-Green Deployment Strategy2m 44s
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5.Canary Deployment Strategy5m 48s
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6.Rolling Back Versions4m 24s
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7.Web API Deployment Strategy3m 14s
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8.Exercise: Serialize a Model6m 44s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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