Google Cloud Skills Boost

Advanced ML: ML Infrastructure

7 Stunden Intermediate universal_currency_alt 16 Guthabenpunkte
Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on GCP.
Skill-Logo für Advanced ML: ML Infrastructure

Schließen Sie diese Aktivität ab und holen Sie sich ein Abzeichen! Treiben Sie Ihre Karriere in der Cloud voran, indem Sie allen zeigen, welche Kompetenzen Sie entwickelt haben.

  • Lab

    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.

  • Lab

    TFX on Google Cloud Vertex Pipelines

    In this lab you will develop, deploy, and run a TFX pipeline on Google Cloud Vertex Pipelines.

  • Lab

    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.).

  • Lab

    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.

  • info
    Informationen zur Aufgabenreihe
    Verfügbare Sprachen