You can check the code for this tutorial here. Once you finish your model experimentation it is time to roll things to production. Rolling Machine Learning to production is not just a question of wrapping the model binaries with a REST API and starting to serve it, but and making it possible to re-create (or update) and re-deploy your model. That means the steps from preprocessing data to training the model to roll it to production (we call this a Machine Learning Pipeline) should be deployed and able to be run as easily as possible while making it possible to track it and parameterize it (to use different data, for example). In this post, we will see how to build a Machine Learning Pipeline for a Deep Learning model using Tensorflow Extended (TFx), how to run and deploy it to Google Vertex AI and why should we use it.

This article was published as a part of the Data Science Blogathon. Cloud computing is a technology that uses the computer system resources like cloud storage, computing power, and they manage data on remote servers and access them via the internet. To know more about Cloud computing. In the last 5 years, the demand for cloud computing keeps on increasing day by day. Many new cloud service providers came to the market. One of the most popular cloud services is the Google cloud platform. In this article, we are going to deep dive into the ML pipeline in GCP (Google cloud platform).

The concepts of neural architecture search and transfer learning are used under the hood to find the best network architecture and the optimal hyperparameter configuration that minimizes the loss function of the model. This article uses Google Cloud AutoML Vision to develop an end-to-end medical image classification model for Pneumonia Detection using Chest X-Ray Images. The dataset is hosted on Kaggle and can be accessed at Chest X-Ray Images (Pneumonia). Go to the cloud console: https://cloud.google.com/ Setup Project APIs, permissions and Cloud Storage bucket to store the image files for modeling and other assets.

In the following steps you create an AI Platform Notebooks instance. In the GCP Console, go to the AI Platform Notebook instances page. On the menu bar, click New Instance add, and then select the TensorFlow 1.x framework. It takes a few minutes for AI Platform Notebooks to create the new instance. Now that you have an AI Platform Notebooks instance, you can download the notebook file for this tutorial.

The concepts of neural architecture search and transfer learning are used under the hood to find the best network architecture and the optimal hyperparameter configuration that minimizes the loss function of the model. This article uses Google Cloud AutoML Vision to develop an end-to-end medical image classification model for Pneumonia Detection using Chest X-Ray Images. The dataset is hosted on Kaggle and can be accessed at Chest X-Ray Images (Pneumonia). Go to the cloud console: https://cloud.google.com/ Setup Project APIs, permissions and Cloud Storage bucket to store the image files for modeling and other assets.