MLOps with Data Version Control: Creating & Using DVC Pipelines

MLOps 2023    |    Expert
  • 12 videos | 1h 21m 21s
  • Includes Assessment
  • Earns a Badge
Data Version Control (DVC) pipelines empower data practitioners to define, automate, and version complex data processing workflows. By streamlining end-to-end processes, pipelines enhance collaboration, maintain data lineage, and enable efficient experimentation and deployment in data-centric projects. In this course, you will discover the intricacies of machine learning (ML) pipelines within DVC. You will set up a pipeline with data cleaning, training, and evaluation stages and run these stages using the dvc repro command. Then you will use DVC to track the status of the pipeline with the help of the dvc.lock file. Next, you will run and track a DVC pipeline as an experiment using DVCLive and view metrics and artifacts of your pipeline in the Iterative Studio user interface. Finally, you will queue DVC experiments so they can be run later, either in parallel or sequentially. This course gives you an in-depth understanding of DVC pipelines, equipping you to seamlessly orchestrate and manage your ML workloads.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Set up a data version control (dvc) project for a machine learning (ml) pipeline
    Track training data in dvc
    Add a data process stage to an ml pipeline
    Run a pipeline stage
    Add a train stage to an ml pipeline
  • Run multiple pipeline stages
    Add an evaluation stage to an ml pipeline
    Remove a duplicate dvc.yaml file
    Run dvc experiment pipelines
    Queue and run experiments sequentially and in parallel
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 54s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 43s
    Find out how to set up a Data Version Control (DVC) project for a machine learning (ML) pipeline. FREE ACCESS
  • Locked
    3.  Tracking Training Data with DVC
    6m 53s
    In this video, you will learn how to track training data in DVC. FREE ACCESS
  • Locked
    4.  Adding the Data Process Stage to the ML Pipeline
    9m 31s
    Discover how to add a data process stage to an ML pipeline. FREE ACCESS
  • Locked
    5.  Executing Pipeline Stages
    10m 11s
    During this video, you will learn how to run a pipeline stage. FREE ACCESS
  • Locked
    6.  Adding a Train Stage to the ML Pipeline
    6m 56s
    In this video, find out how to add a train stage to an ML pipeline. FREE ACCESS
  • Locked
    7.  Executing the Data Process and Train Stages
    6m 41s
    During this video, discover how to run multiple pipeline stages. FREE ACCESS
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    8.  Adding and Executing the Evaluate Stage in a Pipeline
    10m 22s
    Learn how to add an evaluation stage to an ML pipeline. FREE ACCESS
  • Locked
    9.  Eliminating a Duplicate dvc.yaml File
    5m 18s
    Find out how to remove a duplicate dvc.yaml file. FREE ACCESS
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    10.  Running DVC Experiment Pipelines
    8m 35s
    In this video, discover how to run dvc experiment pipelines. FREE ACCESS
  • Locked
    11.  Queueing and Running Experiments
    6m 22s
    During this video, you will learn how to queue and run experiments sequentially and in parallel. FREE ACCESS
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    12.  Course Summary
    1m 54s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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