machine learning

December 19, 2018

Learn from Watson: How Containers Scale AI Workloads

Let's look at how the IBM Cloud Kubernetes Service has been hosting Watson's AI workload so that you can see how AI workloads are different and better in the cloud. 

Continue reading

October 2, 2018

Build Apps with Watson, Blockchain, Kubernetes, and Cloud Foundry

Do you need to select the right technologies to build the next productivity app in your application backlog? Get your hands dirty with those technologies before you start your build. Download the CloudCoins wellness app and use the git repo to tinker under the hood.

Continue reading

March 19, 2018

A predictive Machine Learning model from Build to Retrain

This post is an excerpt from our solution tutorial that walks you through the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. All of this happening in an integrated and unified self-service experience on IBM Cloud.

Continue reading

February 8, 2018

Taming your neural networks: how controlled experimentation can help you build better machine learning models

Businesses today are eager to harness machine learning and deep learning for competitive advantage—yet few businesspeople realize that building a machine learning model or neural network is a marathon, not a sprint.

Continue reading

January 3, 2018

Jump into AI and get NVIDIA Tesla GPUs at a lower price

Graphics processing units (GPUs) take your artificial intelligence (AI) and deep learning workloads to the next level. We'll give you 40 percent off on NVIDIA GPUs when you order an IBM Cloud bare metal server.

Continue reading

October 3, 2017

Lifelong (machine) learning: how automation can help your models get smarter over time

Imagine you’re interviewing a new job applicant who graduated top of their class and has a stellar résumé. They know everything there is to know about the job, and has the skills that your business needs. There’s just one catch: from the moment they join your team, they’ve vowed never to learn anything new again. You probably wouldn’t make that hire, because you know that lifeMachine Learning Brainlong learning is vital if someone is going to add long-term value to your team. Yet when we turn to the field of machine learning, we see companies making a similar mistake all the time. Data scientists work hard to develop, train and test new machine learning models and neural networks. However, once the models get deployed, they don’t learn anything new. After a few weeks or months, become static and stale, and their usefulness as a predictive tool deteriorates.

Continue reading