Watson AI and Data Science on Cloud Pak for Data Series
Accelerate AI-powered growth with your data science investment
As technologies continue to reshape our business, selecting a right data and AI platform can give you a clear edge in growing and innovating at enterprise scale. Powered by the IBM Watson AI technology. Cloud Pak for Data is the cloud native AI platform where you can:
We designed a two-part Webinar series to cover what's new with Cloud Pak for Data. Part 1 features data science and AI development and deployment aspects while Part 2 covers gaining trust in AI models including KPI, explainability, debiasing and model drift. Both sessions are presented live by IBM data science and AI experts.
What's new with data science on Cloud Pak for Data
Learn about Watson Studio Premium, AutoAI, and AI infrastructures
Data science is one of the top use cases on Cloud Pak for Data. Building and scaling AI models is easier on this platform because a business can:
Join us to learn the new features and benefits of data science capabilities on Cloud Pak for Data including AutoAI advancements, AI infrastructures by x86 platforms and IBM Power Systems and other key enhancements.
Distinguished Engineer, Watson Studio | Data Science
IBM Data and AI
Offering Manager, Data and AI Solutions on Power Systems
Data Science and AI SME, Portfolio Lead
IBM Data + AI
Achieve user trust for your AI initiatives
Four stages to consider in managing AI
Pioneers are driving value from AI by building and scaling models tailored to their use cases. When operationalizing AI, gaining user trust and understanding of the potential risks and implications is crucial to scaling adoption and accelerating AI-enabled growth and innovation. AI projects often come to a halt when businesses cannot adequately explain how predictions are derived, or are concerned about monetary and reputational risks arising from known and unknown AI implications. To promote trust and transparency with users and organizations as a whole, you can design the following four stages in your AI strategy:
Stage 1: Establish model metrics tuned to business goals
Stage 2: Explain how models arrive at each output systematically
Stage 3: Mitigate algorithmic biases and unintended consequences
Stage 4: Detect and quantify drift and data inconsistency
As part of this live Webinar, we will be discussing Model Risk Management (MRM) to address supervisory guidance such as SR-11-7 where models be tested and managed for risk. Join us to learn the strategies and methods of putting user trust at the center of managing and measuring models with Watson OpenScale, available as part of IBM Cloud Pak for Data and IBM Cloud.
Program Director, Development
Digital Technical Engagement Lead