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Advanced ML: ML Infrastructure

Advanced 5 Steps 시간 35 크레딧

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.

Data

Quest Outline

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Real Time Machine Learning with Google Cloud ML

Using Cloud DataProc running on a Hadoop cluster you will analyse a data set using Bayes Classification.

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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.

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Distributed Machine Learning with Google Cloud ML

Learn the process for partitioning a data set into two separate parts: a training set to develop a model, and a test set to evaluate the accuracy of the model and then independently evaluate predictive models in a repeatable manner.

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Kubeflow End to End

In this hands-on lab, you will install Kubeflow on an empty Kubernetes Engine cluster and use it to train and serve a sequence-to-sequence model using TensorFlow, Keras, and SeldonIO.

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Awwvision: Cloud Vision API from a Kubernetes Cluster

This hands-on lab uses Kubernetes and Cloud Vision API to create an example of how to use the Vision API to classify (label) images from Reddit’s /r/aww subreddit and display the labelled results in a web app.

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