DevOps for Data Scientists: Data DevOps Concepts

DevOps    |    Intermediate
  • 12 videos | 44m 30s
  • Includes Assessment
  • Earns a Badge
Rating 3.9 of 14 users Rating 3.9 of 14 users (14)
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

  • 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

  • 1m 31s
  • 4m 33s
    In 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
  • Locked
    3.  Data DevOps Topologies
    4m 18s
    Upon 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 Strategy
    3m 39s
    In 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 Tactics
    4m 10s
    Upon 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 Uncertainties
    4m 53s
    In 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 Scaling
    5m 31s
    After 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 Communication
    3m 6s
    In this video, you will learn how DevOps can improve communication for data science workflows. FREE ACCESS
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    9.  Overcoming Ad Hoc DevOps
    2m 50s
    In 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 DevOps
    3m 48s
    Upon 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 Microservices
    3m 22s
    After 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 DevOps
    2m 50s
    Learn how to create a diagram of your data science infrastructure. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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