AI technologies have the power to deliver critical business insights, but maintenance and implementation can dramatically drain organizational resources. Rather than worrying about maintaining complex analytics and AI tools, business leaders are looking to take actions on the insights those technologies bring.
Today, we announce the upcoming general availability of IBM Cloud Pak® for Data as a Service with IBM Cloud Satellite. IBM Cloud Pak for Data as a Service provides integrated data and AI services, fully managed, that simplify and deliver a unified analytics experience across distributed cloud environments.
The integration with IBM Cloud Satellite (a distributed cloud solution that makes it possible to launch cloud services and workloads across multiple environment), helps you run your AI workloads next to your data on clouds in various global geographies. The initial roll-out will allow you to create Jupyter notebooks via Watson Studio on IBM Cloud Pak for Data as a Service and then run those notebooks on AWS in North America. This capability will extend to other regions throughout the year and will eliminate the need to move data around or copy from other public clouds to get insights. Empower your team to efficiently build machine learning models in the cloud, and run models in the same cloud region where your data is stored so you can have a completely seamless data analytics experience.
Quickly uncover value from data
Driving smarter decisions that give customers the best experience is essential to boosting revenue and growth. Businesses need to iterate quickly on the right data, and IBM Cloud Pak for Data as a Service with IBM Cloud Satellite can help speed time to insight so your organization can keep up with the pace of today’s market.
For example, during our beta release, IBM worked with a FinTech firm that helps global investment professionals deliver better outcomes. The firm was looking to use AI to turn millions of data points into actionable investment recommendations. Many of its applications and data sit on AWS while its data and AI tools are consumed as-a-Service from the IBM Cloud.
By co-locating its IBM AI workloads next to its data on AWS, the firm was able to reduce its engineered data scoring latency from 10 seconds to 1 second. This gain in efficiency sped up time to insight and allowed applications to be more actionable and predictive. In the world of FinTech, where markets move second by second with millions of dollars at stake, reducing latency can make a highly significant difference.
Start your path forward
Watch my conversation with IDC on Thursday March 4 at 12:00pm ET, to hear market and IBM perspectives on the evolution of managing data and AI services and why an as a Service approach can help you innovate and stay ahead of the competition. See what successful AI-powered recovery could look like in a post-pandemic world.
Also, in a free one-hour event on March 9, Rob Thomas and other Industry leaders, IBM experts, and clients will discuss the new IBM Cloud Satellite and how to increase developer productivity and shift away from maintaining tools to providing new value to customers.