There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. Now there are three platforms that offer Automated Deep Learning (ADL) so simple that almost anyone can do it. There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. A small percentage of our data science community has chosen the path of learning these new techniques, but it's a major departure both in problem type and technique from the predictive and prescriptive modeling that makes up 90% of what we get paid to do. Artificial intelligence, at least in the true sense of image, video, text, and speech recognition and processing is on everyone's lips but it's still hard to find a data scientist qualified to execute your project.

In order for communities to fight blight, it is essential to know the condition of properties. Historically performing property condition surveys required inspectors to physically visit properties, fill out forms and take pictures. For many communities, this was a slow and expensive process. The Property Condition Survey configuration available with ArcGIS significantly reduces the time and cost associated with performing property surveys by applying artificial intelligence (AI) and machine learning (ML) to street-level property photos and automatically calculate a blight probability for every property. Once blight probabilities are calculated communities can take action against individual properties to fix blight or make policy changes to reverse neighborhoods that are blighted or trending toward blighted.

Last year Custom Vision was released to classify your own objects on images. Yesterday at Build 2018 a new Project Type was added to enable Object Detection in images. In this blog we are going to take a closer look and see what this new feature can do. Detect multiple objects in an image and draw boxes around these objects. As for every Machine Learning project you need a dataset, Kaggle is a great resource for that and I have downloaded The Simpsons dataset. This dataset contains around 7000 images including a CSV file with the coördinates where they are on the pictures.