POWER9: purpose-built systems accelerate AI in the IBM Cloud – open, paid beta

AI, Power servers, Power Systems

Cloud and AI strategies are not one-size-fits-all. From client engagements, we understand the journeys to cloud and AI are complex and unique to each organization. Cloud strategies can include many dimensions of cloud consumption, including public and private, shared, dedicated, on premises and off premises. The average enterprise is leveraging multiple clouds. AI adds another more

Complete your AI puzzle with inference

AI, Power servers, Power Systems

Artificial intelligence is complex, and there are multiple ways to approach an AI initiative. I like to think of AI as a jigsaw puzzle. There are multiple pieces (AI phases) that are dependent on each other in order to realize the picture, and with every piece you get closer to the end goal (business insights). more

Fail fast (and better) in AI development with IBM Watson

AI, IBM Systems Lab Services, Services

Is the fail fast methodology relevant in the context of AI application development? Absolutely. Given that enterprise AI computing is still in its infancy, failing fast with quick turnaround times is critical for the development of AI applications. This entails adopting a flexible end-to-end machine learning pipeline for training workloads at the beginning of the more

New study shows pathway to AI transformation success

AI, Power servers, Power Systems

The promise of artificial intelligence (AI) to reveal valuable insights, reduce time-to-market, re-engineer costs and expand offerings is well understood by executives and technical leaders. What we have not yet defined is how to address the gap between these conceptual ideas and a real, tangible pathway to measurable success. Learning from successful pioneers is vital, more

AI today: Data, training and inferencing

AI, Deep learning, IBM Systems Lab Services

In my last blog, I discussed artificial intelligence, machine learning and deep learning and some of the terms used when discussing them. Today, I’ll focus on how data, training and inferencing are key aspects to those solutions. The large amounts of data available to organizations today have made possible many AI capabilities that once seemed more

AI, machine learning and deep learning: What’s the difference?

AI, Deep learning, IBM Systems Lab Services

It’s not unusual today to see people talking about artificial intelligence (AI). It’s in the media, popular culture, advertising and more. When I was a kid in the 1980s, AI was depicted in Hollywood movies, but its real-world use was unimaginable given the state of technology at that time. While we don’t have robots or more

Accelerating data for NVIDIA GPUs

AI, Big data & analytics, Storage

These days, most AI and big data workloads need more compute power and memory than one node can provide. As both the number of computing nodes and the horsepower of processors and GPUs increases, so does the demand for I/O bandwidth. What was once a computing challenge can now become an I/O challenge. For those more

Game-changing innovation for AI and big data from IBM Storage

AI, Big data & analytics, Storage

Artificial intelligence (AI) is already a world-changing driver of business today and will increasingly be so in the future. IDC predicts that, by 2019, 40 percent of digital transformation (DX) initiatives will use AI services; by 2021, 75 percent of commercial enterprise apps will use AI, over 90 percent of consumers will interact with customer more

Storage made simple for modern data

AI, Big data & analytics, Storage

When we speak with clients, their top storage priorities are very consistent: Storing dramatically more data from novel sources, gaining new business insight and increasing revenue through the use of AI and big data technologies applied to that data. Deploying applications in Red Hat OpenShift and other container environments to help improve application and data more