This learning path consists of step-by-step tutorials and patterns that walk you through the process of streamlining data integration, data governance, analytics, and data virtualization on AWS. It also covers building models on Amazon SageMaker and monitoring these models using IBM OpenScale deployed on SageMaker and IBM Cloud Pak for Data for fairness, quality, and drift metrics, as well as creating data visualizations and dashboards on IBM Cognos Analytics. You can learn a no-code approach for building and deploying machine learning models on Cloud Pak for Data with minimal data science background. Public pandemic data is used to demonstrate how you can build an effective pandemic management system on AWS using Cloud Pak for Data.

Previously, it was the norm for several data-oriented tools, products, and offerings were to be used to address different requirements. Even in the IBM portfolio, enterprises turned to Planning Analytics, Cognos Analytics, and Watson for specific purposes. In some enterprises, this created a situation where there were individual instances of these powerful tools running in the tech ecosystem. While the tools offer great value in themselves, the sense was always "What if we could get all this goodness together?" That's when IBM created a new all-around offering in the form of Cloud Pak for Data.

Leveraging AI-based tools has become an essential piece of the puzzle for many companies in their efforts toward digital transformation--yet this adoption is still often a hurdle for many IT leaders. While 43% businesses reported accelerating AI tools during COVID-19, according to Todd Moore, vice president, Open Technology, IBM, 59% of IT leaders recently admitted that the new technology felt threatening to them, as TechRepublic recently reported. Palantir for IBM Cloud Pak for Data, powered by Red Hat OpenShift, is presenting a solution for this, by giving tools to developers to connect data and AI models, even those without high-level technical expertise. Designed around the "no-code/low-code" framework, which has become increasingly popular as a springboard to launching AI projects, the product integrates IBM Cloud Pak for Data services with Palantir Foundry, a data and analysis platform. No-code offers tools and platforms for simplifying the software development--the kind of software that might be used on platforms like Facebook, Lyft and Google Docs, which was traditionally created with code.

IBM is in the middle of a company transformation. With CEO Arvind Krishna and President Jim Whitehurst at the helm for a year, the company is going all-in on the cloud with its Red Hat and other investments and getting in a better posture for growth and focus with its recent Kyndryl spin-out. At the same time, the company continues to create leading edge research output in hardware, software and the cloud and creating some very innovative products simultaneously. IBM made many announcements prior to THINK. The company announced the world's first 2nm nanosheet device here, check out an interview I had with IBM Cloud leader Howard Boville here, see Z mainframe as a service here, check out Steve McDowell's analysis of Spectrum Fusion here, and see Paul Smith Goodson's coverage of Qiskit Metal for quantum computing here.

IBM Corp. is pushing the envelope on hybrid cloud and artificial intelligence with a number of key announcements early Tuesday ahead of its Think 2021 event, chiefly aimed at accelerating its customer's digital transformation strategies. One of the main highlights of today's announcements is a new Auto SQL capability within IBM's Cloud Pak for Data offering that automates data access and management without needing to move it first. The company also unveiled a new, AI-based tool for modernizing applications and workloads to run in hybrid cloud environments, plus new AI capabilities in Watson and advancements that should help to scale up quantum computing to more use cases. Available Tuesday, the new AutoSQL capability for IBM Cloud Pak for Data is a big deal because it enables companies to automate access, integration and management of their data no matter where it resides, the company said. IBM said it's addressing one of the most critical pain points customers face as they attempt to reduce the complexity of curating data for AI.

Every once in awhile an idea comes along that's so good it makes you wonder why it took so long for someone to think of it. IBM's AI Fact Sheets is one of those ideas. AI Fact Sheets are a lot like packaged food nutrition labels. They contain information about an AI model's development, capabilities, benchmark performance, and more. Big Blue today announced its plans to "commercialize key automated documentation capabilities from IBM Research's AI Factsheets methodology into Watson Studio in Cloud Pak for Data throughout 2021." In other words: businesses and developers using Watson Studio in Cloud Pak for Data will soon have access to an automated AI Fact Sheets tool to create transparency and info reports.

IBM has unveiled a slew of announcements designed to help businesses scale their use of AI. The company also announced the rollout of new capabilities for its Watson platform. IBM researchers have built a hybrid question-answering system called Neuro-Symbolic-QA (NSQA) that for the first time uses neurosymbolic AI to allow an AI system to offer "and"/ "or" to its recommendations. This will ultimately position the system to perform better in real-world situations, IBM said. "This enhanced reasoning capability comes as a result of an entirely new foundational AI method created by IBM researchers called Logical Neural Networks (LNN), IBM said. LNNs are a modification of today's neural networks so that they become equivalent to a set of logic statements, but they also retain the original learning capability of a neural network, the company explained in a blog post. QA is designed to meet the significant challenges in language-based AI, in particular the fact that the training of NLP ...

During IBM's virtual AI Summit this week, the company announced updates across its Watson family of products in the areas of language, explainability, and workplace automation. A new feature called Reading Comprehension surfaces answers from databases of enterprise documents in response to natural language questions, assigning a confidence score to each response. A novel module in Watson Assistant called FAQ Extraction automatically generates question-and-answer documents. And AI Factsheets automatically captures key facts on a machine learning model's performance and generates reports to "foster transparency and ensure compliance." According to IBM, Reading Comprehension, which was built atop a top-performing question-answering system from IBM Research, is intended to help identify more precise answers in response to queries referring to business documents.

IBM Corp. today introduced new versions of its Cloud Pak for Data and Cloud Pak for Automation products that will enable enterprises to harness deep learning models in their operations more easily. The Cloud Pak product family is a set of software solutions designed to streamline a variety of tasks ranging from cybersecurity to data analytics. The entire lineup is based on Red Hat OpenShift. Thanks to OpenShift, the solutions can run both in the public cloud and on on-premises infrastructure. Deep learning is a branch of artificial intelligence that uses artificial neural networks to learn from massive amounts of data. IBM Chief Executive Arvind Krishna (pictured) in October named the hybrid cloud and artificial intelligence as core pillars of the company's revenue growth strategy.

Every sector in the world is currently investigating artificial intelligence (AI). Groundbreaking advancements in AI will take place in health and life sciences, cyber-intelligence, smart cities and transportation. But before anyone can execute an AI strategy, they'll need a data strategy. When we think about the vast majority of the work that data scientists do, 85% of that work is associated with data governance, cleansing, tagging and classification of data--all elements that are embodied in the concept of a data strategy. Thus, harnessing the power of data with AI requires understanding the data sets required and how relevant they are to the insights we are trying to drive.