Artificial intelligence in the future, at least represented in science fiction, can learn, interpret, and take action based on data analysis. AI in production is the present, a present that feels decidedly futuristic. Jonathan Ballon explains why Intels leading portfolio of AI and computer vision edge technology will drive advances that improve how we work and live.

Tobias Uldall-Espersen and Thomas Krogsgaard Holme explain how they applied microservice architecture and privacy by design principles to break down a monolithic portal containing 50+ productsthe Danish national ehealth portal Sundhed.dkredesign it, and produce a scalable and flexible platform in compliance with the EU General Data Protection Regulation (GDPR).

Christian Monaghan explains how he and his team successfully migrated HealthCare.gov, America's largest government website, to the cloud infrastructure provisioning tool Terraform, shares lessons learned along the way, and details how you can effectively use Terraform for your next project.

Drawing on his experience working with customers across many industries, including chemical sciences, healthcare, and oil and gas, Jim Scott details the major impediments to successful completion of deep learning projects and solutions while walking you through a customer use case.

Ward Eldred offers an overview of the types of analytical problems that can be solved using deep learning and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects.

The power of big data continues to modernize traditional industries, including healthcare. Ian Brooks explains how to implement intelligent preventive screening for conditions by applying electronic medical records (EMR) to predictive analytics via supervised machine learning techniques.

Jim Scott details relevant use cases for blockchain-based solutions across a variety of industries, focusing on a suggested architecture to achieve high-transaction-rate private blockchains and decentralized applications backed by a blockchain. Along the way, Jim compares public and private blockchain architectures.

AI-powered chatbots are increasingly becoming viable solutions for customer service use cases. Technology leaders must consider adopting a multichannel chatbot strategy to avoid siloed chatbot solutions. Sharad Gupta shares a framework to ensure long-term strategic investment in chatbots.

Deep Learning (DL) is a computational intense form of machine learning that has revolutionize many fields including computer vision, automated speech recognition, natural language processing and artif …

The blockchain is a formerly niche idea thats on the path to becoming a standard technology (think responsive design or containers). The sweet spot for the blockchain is transactional data. Using an ownership-tracking example, Erin Morrissey walks you through the technical ideas behind the blockchain to show how each contributes and explains why any of it even matters.

Interpretable models result in more accurate, safer, and more profitable machine learning products, but interpretability can be hard to ensure. Michael Lee Williams examines the growing business case for interpretability, explores concrete applications including churn, finance, and healthcare, and demonstrates the use of LIME, an open source, model-agnostic tool you can apply to your models today.

Artificial intelligence is transforming every industry, but the role it will play in healthcare is profound. Arjun Bansal explains how AI can give physicians new insights and speed time to diagnosis by leveraging vast amounts of healthcare data and how it can reduce the time and money spent to develop new medicines.

Deep learning with ConvNet in particular has emerged as a promising tool in medical research labs and diagnostic centers to help analyze images and scans, and systems are now surpassing human capability for manual inspection. Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis.

Image recognition classification of diseases will minimize the possibility of medical mistakes, improve patient treatment, and speed up patient diagnosis. Mauro Damo and Wei Lin offer an overview of an approach to identify bladder cancer in patients using nonsupervised and supervised machine learning techniques on more than 5,000 magnetic resonance images from the Cancer Imaging Archive.

The 2012 Obama campaign ran the first personalized presidential campaign in history. The data team was made up of people from diverse backgrounds who embraced data science in service of the goal. Civis Analytics emerged from this team and today enables organizations to use the same methods outside politics. Katie Malone shares lessons learned from these experiences for building effective teams.

Blockchain continues to be an innovative force in healthcare and as it gains momentum, the industry is learning more about the benefits possible with this technology, including improved workflows, red …

Lightning talk featuring Truu (formerly known as Doctors Link) showcasing the impact that decentralized, self-sovereign identity and verifiable credentials can have on healthcare. Truu allows doctors …