DevOps for Data Scientists: Data DevOps Concepts
DevOps
| Intermediate
- 12 videos | 44m 30s
- Includes Assessment
- Earns a Badge
To carry out DevOps for data science, you need to extend the ideas of DevOps to be compatible with the processes of data science and machine learning (ML). In this 12-video course, learners explore the concepts behind integrating data and DevOps. Begin by looking at applications of DevOps for data science and ML. Then examine topological considerations for data science and DevOps. This leads into applying the high-level organizational and cultural strategies for data science with DevOps, and taking a look at day-to-day tasks of DevOps for data science. Examine the technological risks and uncertainties when implementing DevOps for data science and scaling approaches to data science in terms of DevOps computing elements. Learn how DevOps can improve communication for data science workflows and how it can also help overcome ad hoc approaches to data science. The considerations for ETL (Extract, Transform, and Load) pipeline workload improvements through DevOps and the microservice approach to ML are also covered. The exercise involves creating a diagram of data science infrastructure.
WHAT YOU WILL LEARN
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Discover the subject areas covered in this course
Define the use and application of devops for data science and machine learning
Describe topological considerations for data science and devops
Apply high-level organizational and cultural strategies for data science with devops
Describe the specific day-to-day tasks of devops for data science
Assess technological risks and uncertainties when implementing devops for data science -
Describe scaling approaches to data science using devops
Identify how devops can improve communication for data science workflows
Identify how devops can help overcome ad hoc approaches to data science
Describe considerations for etl pipeline workflow improvements through devops
Describe the microservice approach to machine learning
Create a diagram of your data science infrastructure
IN THIS COURSE
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1m 31s
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4m 33sIn this video, you will learn about DevOps for data science and machine learning, including how to use it and what it's for. FREE ACCESS
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3. Data DevOps Topologies4m 18sUpon completion of this video, you will be able to describe topological considerations for data science and DevOps. FREE ACCESS
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4. Data DevOps Strategy3m 39sIn this video, find out how to apply high-level organizational and cultural strategies for data science with DevOps. FREE ACCESS
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5. Data DevOps Tactics4m 10sUpon completion of this video, you will be able to describe the specific day-to-day tasks of DevOps for data science. FREE ACCESS
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6. Data DevOps Uncertainties4m 53sIn this video, you will learn how to assess technological risks and uncertainties when implementing DevOps for data science. FREE ACCESS
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7. Data DevOps Scaling5m 31sAfter completing this video, you will be able to describe scaling approaches to data science using DevOps. FREE ACCESS
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8. Data DevOps Communication3m 6sIn this video, you will learn how DevOps can improve communication for data science workflows. FREE ACCESS
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9. Overcoming Ad Hoc DevOps2m 50sIn this video, you will learn how DevOps can help overcome ad hoc approaches to data science. FREE ACCESS
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10. ETL Pipeline DevOps3m 48sUpon completion of this video, you will be able to describe considerations for improving ETL pipeline workflow through DevOps. FREE ACCESS
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11. Data DevOps Microservices3m 22sAfter completing this video, you will be able to describe the microservice approach to machine learning. FREE ACCESS
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12. Exercise: Topological View of Data Science DevOps2m 50sLearn how to create a diagram of your data science infrastructure. 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.
