Скачать или смотреть Vertex Pipelines Qwik Start GSP965

Overview
Pipelines help you automate and reproduce your ML workflow. Vertex AI integrates the ML offerings across Google Cloud into a seamless development experience. Previously, models trained with AutoML and custom models were accessible via separate services. Vertex AI combines both into a single API, along with other new products. Vertex AI also includes a variety of MLOps products, like Vertex Pipelines. In this lab, you will learn how to create and run ML pipelines with Vertex Pipelines.

Why are ML pipelines useful?
Before diving in, first understand why you would want to use a pipeline. Imagine you're building out a ML workflow that includes processing data, training a model, hyperparameter tuning, evaluation, and model deployment. Each of these steps may have different dependencies, which may become unwieldy if you treat the entire workflow as a monolith. As you begin to scale your ML process, you might want to share your ML workflow with others on your team so they can run it and contribute code. Without a reliable, reproducible process, this can become difficult. With pipelines, each step in your ML process is its own container. This lets you develop steps independently and track the input and output from each step in a reproducible way. You can also schedule or trigger runs of your pipeline based on other events in your Cloud environment, like when new training data is available.

What you'll learn
Use the Kubeflow Pipelines SDK to build scalable ML pipelines
Create and run a 3-step intro pipeline that takes text input
Create and run a pipeline that trains, evaluates, and deploys an AutoML classification model
Use pre-built components for interacting with Vertex AI services, provided through the google_cloud_pipeline_components library
Schedule a pipeline job with Cloud Scheduler
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