Machine Learning has a reputation for demanding lots of data and powerful GPU computations. This leads many people to believe that building custom machine learning models for their specific dataset is impractical without a large investment of time and resources. In fact, you can leverage Transfer Learning on the web to train an accurate image classifier in less than a minute with just a few labeled images. Teaching a machine to classify images has a wide range of practical applications. You may have seen image classification at work in your photos app, automatically suggesting friends or locations for tagging.
Smart Assistants, fancy image filters in Snapchat and apps like Prisma all have one thing in common--they are powered by Machine Learning. The use of Machine Learning in mobile apps is growing and new mobile apps are developed with Machine Learning based services as business models. In this blog series we want to give you hands-on advice on how you can train and deploy a convolutional neural network for image classification to a mobile app using the popular machine learning framework TensorFlow Mobile. Our task will be to classify images of houseplants which we have collected ourselves. You don't have to go and snap pictures of plants, however, because our approach is generic and can be used for training and deploying a convolutional neural network for image classification, independent of their subject.
Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using SageMaker. These models can be quickly deployed and are pre-trained open-source models from PyTorch Hub and TensorFlow Hub. These models solve common ML tasks such as image classification, object detection, text classification, sentence pair classification, and question answering. The example notebooks show you how to use the 17 SageMaker built-in algorithms and other features of SageMaker.