Today’s data science and analytics teams are often composed of individuals with a variety of skill sets, educational backgrounds, levels of exposure to open source tools and professional needs. Here’s a typical breakdown:
Business professionals need straightforward ways to first discover and then
Recently, I sat down with Kyle Weeks, Program Director for Ecosystems in Data Science and AI. I wanted to review some exciting new opportunities made possible by several recent developments in IBM Data Science:
AutoAI, a powerful automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award, chosen by a panel of 13 independent judges yesterday for the AIconics AI Summit in San Francisco.
68 percent of surveyed businesses recently responded that they use machine learning (ML) or plan to do so in the next three years. AI technologies rapidly are becoming how businesses distinguish themselves from competitors. But choosing the best way to implement AI isn’t always a straightforward
Will AI take over the world? Or, more to the point, will it take over the humankind? It seems to have invaded the public consciousness, sparking concerns that AI will take away jobs. This fear is driven in part by companies using AI to deliver cost savings across their businesses, including areas
The best data catalogs can automate the process to collect, classify and profile data to ensure the highest standards of quality. Here are three popular use cases detailing why companies are moving towards IBM’s Watson Knowledge Catalog.
Artificial intelligence and machine learning (ML) have become very popular recently due to their ability to both optimize processes and provide the deep insights that push enterprises and industries forward. In fact, 68 percent of respondents in a recent 451 Research Report, Accelerating AI with
Is your business ready to harness the power of AI to unlock value from all your data? Are you just getting started on your company’s path to AI – or are you working to scale AI to more areas of your enterprise business?
Regardless of where you are on your journey to AI – you should consider
In my last blog post, I covered how you can deliver an AI pilot in just eight weeks and at the same time design your program in a way to scale the AI across your enterprise. Culture, architecture and technology is fundamental to move from AI pilot to AI @ Scale. I also discussed how IBM is helping
IBM continues to increase support for open source technologies. Today, we are pleased to announce that Cloud Pak for Data System now features a new capability for Postgres workloads—the IBM Performance Server for PostgreSQL.
In a previous blog, I explained how data science capabilities, massive parallel processing (MPP)
and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?
Research firm Quark +
IBM’s integrated platform for Data & AI, which is 100% complimentary to Red Hat offerings. It runs on OpenShift today and has a hardware version called Cloud Pak for Data System. The beauty of Cloud Pak for Data is that it includes all of IBM’s strategic Data and AI services – including Watson