More and more companies are starting to use geospatial data for their machine learning applications to draw insights from the patterns of life. To better understand how they do this, we'll discuss what exactly is meant with Geospatial Artificial Intelligence (GeoAI). We'll cover the tasks that form part of (geospatial) machine learning and deep learning workflows, the prerequisites to perform these, and give an overview of the current tools and initiatives in the open source GIS community to integrate machine learning and deep learning into existing workflows. Artificial Intelligence is the science and engineering of making machines intelligent, so that they can achieve a task the way humans do. While true AI does not exist (yet), AI subfields are improving rapidly and already changing the way companies understand how people interact with their environment and how they make predictions based on the patterns they discover in their data, such as predicting traffic patterns or housing prices, or simply classifying large quantities of imagery data.

With a greater number of users integrating deep learning into their imagery workflows, the traditional components of labeling, training and inferencing have become increasingly tedious. With ArcGIS Enterprise 11.0, releasing on July 21, 2022, ArcGIS Image Server will include Deep Learning Studio, a new web application that streamlines the entire deep learning workflow. This web app provides a multi-user, project-based collaborative environment for users to collect training samples, train deep learning models, and run inferencing at scale, all through a web browser. In the following video, watch a quick demo of how it all works. Deep Learning Studio offers a project-based space in which all components of the deep learning workflow, including user groups, are managed efficiently.

Deep Learning (DL) techniques have changed the field of computer vision significantly during the last decade, providing state-of-the-art solutions for classical tasks (e.g., object detection and image classification) and opening the doors for solving challenging new problems, such as image-to-image translation and visual question answering (VQA). The success and popularization of DL in computer vision and related areas (e.g., medical image analysis) has been fostered, in great part, by the availability of rich tools, apps and frameworks in the Python and MATLAB ecosystems. In this blog post, I will show how your team can use both MATLAB and Python effectively and provide an easy-to-follow recipe that you should allow you to leverage "the best of both worlds" when building computer vision solutions using deep learning. Python is a programming language created by Guido van Rossum in the early 1990s. It has been adopted by many data scientists and machine/deep learning researchers thanks to popular packages (e.g., scikit-learn) and frameworks (e.g., Keras, TensorFlow, PyTorch).

Making machine learning operations easier to use, manage and organize for enterprises has always been the goal of the series of best practices known as MLOps. But while MLOps works well for the needed processes and commodity CPU-based infrastructure of traditional machine learning, it can come up short in being as useful for more complex deep learning workloads, which can be far larger and more demanding than traditional machine learning requirements. To fill this gap, New York-based startup, Spell, has launched what it calls a cloud-agnostic MLOps platform that is targeted to serve the more complex and unique needs of deep learning using the principles of MLOps used for machine learning. "With deep learning, there aren't a lot of options, because people are using their own tools," Tim Negris, the head of marketing for Spell, told EnterpriseAI. But using the company's newly-unveiled platform, enterprises can now more easily manage their deep learning model training, orchestration, monitoring, reporting, dashboarding and more, he said.

Deep learning, or using massive amounts of data to build intelligent models, is a hot topic. Many companies, now seeing the benefits of AI materialize, have decided they need to get started on deep learning or risk getting left behind. How do you connect the big idea (businesses need to get started on deep learning) to the specific topic at hand (set up a workflow)? Here are the five main steps on setting up a deep learning workflow. Computers are great at optimizing models, but not so great at setting strategic goals.

The OLCF's recently deployed DGX-1 artificial intelligence supercomputer by NVIDIA, featuring eight NVIDIA Tesla GPUs and NVLink technology, will offer scientists and researchers new opportunities to delve into deep learning technologies. The Oak Ridge Leadership Computing Facility (OLCF) recently deployed a new NVIDIA DGX‑1 artificial intelligence supercomputer to offer scientists and researchers opportunities to delve into deep learning technologies with more vigor than ever before. Deep learning uses neural networks to classify data or predict outcomes by training models on large data sets and by abstracting high-level features or patterns from lower level data. The OLCF is a DOE Office of Science User Facility located at ORNL. Scientists and researchers at the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) are using deep learning because of its potential to leverage big data analytics to automate and accelerate the scientific discovery process.

Deep learning is getting lots of attention lately, and for good reason. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks. The term "deep" usually refers to the number of hidden layers in the neural network.