Machine Learning App Examples

Machine Learning powers almost every internet service we use these days, but it’s rare to find a full pipeline example of machine learning being deployed in a web app. In this episode, I’d like to present 5 full-stack machine learning demos submitted as midterm projects from the students of my current course. The midterm assignment was to create a paid machine learning web app, and after receiving countless incredible submissions, I’ve decided to share my favorite 5 publicly. I was surprised by how many students in the course had never coded before and to see them building a full-stack web app in a few weeks was a very fulfilling experience. Use these examples as a template to help you ideate on potential business ideas to make a positive impact in the world using machine learning. And if you’d like, be sure to reach out and support each of the students I’ve demoed here today in any way can you offer.

Why deploy AI/ML (Artificial Intelligence & Machine Learning) workloads on OpenShift?

While organizations are turning to Artificial Intelligence and Machine Learning (AI/ML) to better serve customers, reduce cost, and gain other competitive advantages, there are significant challenges to executing these programs.

Data Scientists need a self-service experience that allows them to build, scale, and share their machine learning (ML) modeling results across the hybrid cloud.

With Red Hat OpenShift, you’ll enable data scientists to easily enable and deploy their ML modeling without the dependency on IT to provision infrastructure.

Introducing Autonomous Economic Agents: What is an AEA?

Autonomous Economic Agents (AEAs) are adaptive independent programs that have a narrowly defined goal to produce some economic gain for their owner. These agents will be at the forefront of the next industrial revolution, disrupting billion-dollar industries and creating innovative new solutions.

Fetch.ai is a world-changing project, a “decentralized digital world” where autonomous software agents act on the behalf of their owners, or themselves, to get useful economic work done. They consider themselves to be a “dynamic, fast-growing international team of experts and forward-thinking technology enthusiasts working on the convergence of blockchain, AI and multi-agent systems.”

They are building a collective super-intelligence on top of decentralized economic internet built with a highly scalable next-generation distributed ledger technology. Combined with machine learning, this delivers the predictions and infrastructure to power the future economy.

Why creativity is being liberated by data and machine learning

Are machine learning and creativity at odds? In a word: No. And don’t just take it from Google. They sat down with Justin Billingsley, CEO at Publicis Emil; Dawn Winchester, chief digital officer at Publicis North America; Andrew Shebbeare, co-founder and chairman of Essence; and Vijay Sharma, FlipKart’s head of digital media and brand marketing. They explain how creativity is being empowered by the most recent advances in technology, and why great creatives love data and automation.

How robotics (and AI) are changing how we work and live | TECH(talk)

Robotics and AI play increasingly important roles in a variety of industries and no, they’re not here to take over mankind. Keith Shaw, editor-in-chief of Robotics Business Review, joins Ken Mingis and Juliet Beauchamp to discuss the state of robotics in the enterprise. Currently, robots are good at highly specific tasks. But we’re not far away from drones, autonomous vehicles and surgical robots becoming critical parts of daily life.

Artificial Intelligence VS Machine Learning

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.

Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.