MLOps with Data Version Control: Creating & Using DVC Pipelines

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. 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.