Скачать с ютуб End-To-End: Pipeline Orchestration (KFP) - AutoML in Vertex AI for ML Operations [notebook 02c] в хорошем качестве

End-To-End: Pipeline Orchestration (KFP) - AutoML in Vertex AI for ML Operations [notebook 02c]

An end-to-end workflow using Pipelines within Vertex AI on Google Cloud Platform. We will use AutoML to train a machine learning model. A walkthrough of building a repeatable pipeline to orchestrate all the steps from connecting to data sources, training a model, evaluating the final model, deploying to an online endpoint and requesting predictions from multiple clients. A few deep dives along the way including model explainability! This video follows the notebook 02c - Vertex AI - Pipelines - AutoML with clients (code) in an automated pipeline. GitHub Repository: https://github.com/statmike/vertex-ai... The Notebook followed in this video: https://github.com/statmike/vertex-ai... Timeline: 0:00 - Introduction 1:00 - Overview 3:50 - Start Walkthrough 5:36 - [Notebook Section] Setup 10:30 - [Notebook Section] Pipeline Definition (part 1) 11:13 - Q&A: AutoML model types? 16:08 - [Notebook Section] Pipeline Definition (part 2) 20:27 - Q&A: What optimization objective? 23:36 - [Notebook Section] Pipeline Definition (part 3) 26:38 - [Notebook Section] Compile Pipeline 28:00 - [Notebook Section] Create Vertex AI Pipeline Job 30:40 - Review Completed Pipeline 35:40 - [Notebook Section] Prediction 44:57 - [Notebook Section] Batch Prediction 47:00 - [Notebook Section] Explanations (part 1) 47:17 - Q&A: What are explanations? 50:50 - [Notebook Section] Explanations (part 2) 53:21 - Q&A: When should I use pipelines for AutoML? 56:04 - Wrap-up