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.
Until recently, the use of artificial intelligence (AI) was only possible with great effort and construction of own neural networks. Today, the barrier to entering the world of AI through cloud computing services has fallen dramatically. Thus, one can immediately use current AI technology for the (partial) automation of the quality control of components without having to invest heavily in AI research. In this article, we show how such an AI system can be implemented exemplarily on the Google Cloud Platform (GCP). For this purpose, we train a model using AutoML and integrate it perspectively using Cloud Functions and App Engine into a process where manual corrections in quality control are possible.
Google's AutoML lets you train custom machine learning models without having to code Training high-performance deep networks is often a big task especially for those who have less experience in deep learning or AI. Also, we might require GPU in addition to RAM and CPU. I experienced a lot of issues while trying to classify with CNN. What if I said Google AutoML Vision will solve our problems? Yes, AutoML Vision enables us to train custom machine learning models to classify our images according to our own defined labels.
Google Colaboratory or Colab has been one of the favorite development environment for ML beginners as well as researchers. It is a cloud-based Jupyter notebook do there have to be some awesome ways of loading machine learning data right from your local machine to the Cloud. We'll be discussing some methods which would avoid you to click the "Upload" button directly! If you are working on a project which has its own dataset like any object detection model, classification models etc. then we will like to pull the dataset from GitHub directly. If the dataset is in an archive ( .zip or .tar
Leveraging machine learning to process data and workloads has proved to be significantly beneficial for diverse enterprise industries in recent years. Whether it be healthcare, BFSI or retail, machine learning systems turned out to be extremely promising to process millions of data and build complex models. Having said that, the traditional machine learning process involves humans to look after the operations, to code, and to build the models. But, with the crisis in hand, businesses are looking to reduce their workforce, some are even not equipped with resources to spend on employing an experienced data science team. And that's when AutoML can come to rescue for many.