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.
Using Cloud DataProc running on a Hadoop cluster you will analyse a data set using Bayes Classification.
In this lab you will build a simple scikit-learn model, upload the model to AI Platform Prediction, and make predictions against the model.
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.
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.
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.