Earlier in this decade, when the hyperscalers and the academics that run with them were building machine learning frameworks to transpose all kinds of data from one format to another – speech to text, text to speech, image to text, video to text, and so on – they were doing so not just for scientific curiosity. They were trying to solve real business problems and addressing the needs of customers using their software. At the same time, IBM was trying to solve a different problem, naming creating a question-answer system that would anthropomorphize the search engine. This effort was known as Project Blue J inside of IBM (not to be confused with the open source BlueJ integrated development environment for Java), was wrapped up into a software stack called DeepQA by IBM. It was this DeepQA stack, which was based on the open source Hadoop unstructured data storage and analytics engine that came out of Yahoo and another project called Apache UIMA, which predates Hadoop by several years and which was designed by IBM database experts in the early 2000s to process unstructured data like text, audio, and video.

Last week, I talked about how implementing AI solutions in your organization is a lot like climbing a ladder–going it alone can be risky, but a good AI infrastructure is like having a trustworthy friend to help steady your climb. In my view, a crucial piece of that infrastructure support is the IBM Power AI Enterprise Platform. This new technology offers a comprehensive infrastructure environment designed to address the key requirements for AI initiatives. In other words, to help you start climbing the AI Ladder with confidence and speed, mitigating risk. Let's take a look at how PowerAI Enterprise helps accelerate each step of your AI journey.

Today I am tremendously excited to announce IBM Cognitive Systems' newest applied artificial intelligence (AI) offering, PowerAI Enterprise. This new platform extends all the capability we have been packing into our distribution of deep learning and machine learning frameworks, PowerAI, by adding tools which span the entire model development workflow. With these capabilities, customers are positioned to develop better models more quickly, and as their requirements grow efficiently scale and share data science infrastructure. All machine learning and deep learning models train on large amounts of data. Fortunately (and unfortunately), organizations are swimming in data sitting in structured and unstructured forms, and beyond the data they have under their control, organizations also have access to data for free or for a fee from a variety of sources.