MLOps with Data Version Control: Working with Pipelines & DVCLive

MLOps 2023    |    Beginner
  • 17 videos | 2h 11m 52s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Data Version Control (DVC) pipelines enable the construction of end-to-end data processing workflows, connecting data and code stages while maintaining version control. DVCLive is a Python library for logging machine learning metrics in simple file formats and is fully compatible with DVC. In this course, you will configure and employ pipelines in DVC and modularize and coordinate each step, while leveraging the dvc.yaml file for stage management and the dvc.lock file for project consistency. Next, you will dive into practical DVC utilization with Jupyter notebooks. You will track model parameters, metrics, and artifacts via Python code's log statements using DVCLive. Then you will explore the user-friendly Iterative Studio interface. Finally, you will leverage DVCLive for comprehensive model experimentation. By pushing experiment files to DVC and employing Git branches, you will manage parallel developments. You will pull requests to streamline merging experiment branches and register model artifacts with the Iterative Studio registry. This course will equip you with the foundational knowledge of DVC and enable you to automate the tracking of model metrics and parameters with DVCLive.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Set up an ml pipeline stage
    Add a stage to an ml pipeline
    Use the dvc.lock file
    Execute a dvc pipeline
    Set up dvc in a jupyter notebook
    Set up an iterative studio project
    Visualize data for ml
    Log plots to dvclive
  • Log and track images in dvclive
    Track ml experiments using dvclive
    Push experiment files to dvc and github
    Merge a branch with main in github
    Run and track a k-nearest neighbor (knn) regression model
    Log a model as an artifact
    Register a model in the iterative studio registry
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 5s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 11m 40s
    During this video, you will learn how to set up an ML pipeline stage. FREE ACCESS
  • Locked
    3.  Adding a Stage to a Data Version Control (DVC) Pipeline
    10m 3s
    In this video, find out how to add a stage to an ML pipeline. FREE ACCESS
  • Locked
    4.  Using the dvc.lock File
    3m 43s
    Discover how to use the dvc.lock file. FREE ACCESS
  • Locked
    5.  Executing a DVC Pipeline
    7m 37s
    Find out how to execute a DVC pipeline. FREE ACCESS
  • Locked
    6.  Setting up a DVC Project for Regression Analysis
    7m 52s
    In this video, you will learn how to set up DVC in a Jupyter Notebook. FREE ACCESS
  • Locked
    7.  Setting up Iterative Studio and DVCLive
    9m 59s
    During this video, discover how to set up an Iterative Studio project. FREE ACCESS
  • Locked
    8.  Setting up Data for Visualizing and Tracking Using DVC
    4m 28s
    In this video, find out how to visualize data for ML. FREE ACCESS
  • Locked
    9.  Logging Plots Using DVCLive
    12m 31s
    Learn how to log plots to DVCLive. FREE ACCESS
  • Locked
    10.  Logging and Tracking Images Using DVCLive
    7m 42s
    During this video, discover how to log and track images in DVCLive. FREE ACCESS
  • Locked
    11.  Tracking Experiments with DVCLive
    10m 13s
    In this video, you will learn how to track ML experiments using DVCLive. FREE ACCESS
  • Locked
    12.  Pushing Experiment Files to DVC
    9m 21s
    Find out how to push experiment files to DVC and GitHub. FREE ACCESS
  • Locked
    13.  Committing a Pull Request to Merge Experiment Details
    7m 8s
    Discover how to merge a branch with main in GitHub. FREE ACCESS
  • Locked
    14.  Running and Tracking a kNN Regression Experiment with DVC
    6m 7s
    During this video, you will learn how to run and track a k-Nearest Neighbor (kNN) regression model. FREE ACCESS
  • Locked
    15.  Tracking Model Artifacts
    11m 33s
    Find out how to log a model as an artifact. FREE ACCESS
  • Locked
    16.  Registering Models with the Studio Registry
    7m 45s
    In this video, discover how to register a model in the Iterative Studio registry. FREE ACCESS
  • Locked
    17.  Course Summary
    2m 6s
    In this video, we will summarize the key concepts covered in this course. 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.

YOU MIGHT ALSO LIKE

Course MLOps with MLflow: Using MLflow Projects & Recipes
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Book Managing Datasets and Models
Journey MLOps
Rating 4.7 of 6 users Rating 4.7 of 6 users (6)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course MLOps with Data Version Control: Tracking & Serving Models with DVC & MLEM
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Course Advancing DEI with Agility
Rating 4.4 of 902 users Rating 4.4 of 902 users (902)
Course AWS Developer Associate 2022: Identity & Access Management
Rating 4.5 of 42 users Rating 4.5 of 42 users (42)