Advanced ML: ML Infrastructure
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Vertex AI: Qwik Start
In this lab, you will use BigQuery for data processing and exploratory data analysis, and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value (CLV). The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. Starting with a local BigQuery and TensorFlow workflow, you will progress toward training and deploying your model in the cloud with Vertex AI.
TFX on Google Cloud Vertex Pipelines
In this lab you will develop, deploy, and run a TFX pipeline on Google Cloud Vertex Pipelines.
Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes
AutoML Vision helps developers with limited ML expertise train high quality image recognition models. In this hands-on lab, you will learn how to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.).
Implementing Canary Releases of TensorFlow Model Deployments with Kubernetes and Anthos Service Mesh
In this lab you will install the Anthos Service Mesh, and deploy a resnet model, all on a GKE cluster.