Advanced ML: ML Infrastructure

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.

• 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.
• 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 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.).