Google Cloud Skills Boost

Advanced ML: ML Infrastructure

8 hours Advanced universal_currency_alt 21 Credits
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.
Badge for Advanced ML: ML Infrastructure

When you complete this activity, you can earn the badge displayed above! View all the badges you have earned by visiting your profile page. Boost your cloud career by showing the world the skills you have developed!

  • Lab

    Scikit-learn Model Serving with Online Prediction Using AI Platform

    In this lab you will build a simple scikit-learn model, upload the model to AI Platform Prediction, and make predictions against the model.

    1 hour
    Introductory
    1 Credit
    • English
    • español (Latinoamérica)
    • français
    • 日本語
    • português (Brasil)
  • 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.

    1 hour 30 minutes
    intermediate
    5 Credits
    • English
    • español (Latinoamérica)
    • français
    • 日本語
    • português (Brasil)
  • Lab

    TFX on Google Cloud Vertex Pipelines

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

    1 hour 30 minutes
    intermediate
    5 Credits
    • English
    • 日本語
  • 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.).

    1 hour 30 minutes
    intermediate
    5 Credits
  • Lab

    Implementing Canary Releases of TensorFlow Model Deployments with Kubernetes and Istio

    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.).

    1 hour 30 minutes
    intermediate
    5 Credits
    • info
    Quest Info
    Available languages
    English
    What do I do when I finish this quest?
    After finishing this quest, you can explore additional content in your learning path or browse the learning catalog.
    What badges can I earn?
    Upon finishing a quest, you will earn a badge of completion. Some quests test your ability to apply your knowledge via a final assessment challenge lab. For these quests, you will receive a skill badge. Badges can be viewed on your profile and shared with your social network.