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Ensuring Responsible AI Across the Entire ML Lifecycle

Dataiku

When AI failures make headlines because they have created unanticipated and potentially problematic outcomes, this is not unique to one specific use case or industry. If you are utilizing AI, this is something that is likely on your radar, but having good intentions with AI utilization is simply not enough.

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How the Masters uses watsonx to manage its AI lifecycle

IBM Big Data Hub

Through a partnership spanning more than 25 years, IBM has helped the Augusta National Golf Club capture, analyze, distribute and use data to bring fans closer to the action, culminating in the AI-powered Masters digital experience and mobile app. At the Masters®, storied tradition meets state-of-the-art technology.

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Exploring the hyper-competitive future of customer experience

IBM Big Data Hub

The future of customer experience must be intertwined with customer service to keep pace with customer needs to ensure organizations are delivering high customer satisfaction. And yet, organizations have historically failed to ensure all relevant employees have the right information on hand to make important decisions.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. FMs are multimodal; they work with different data types such as text, video, audio, and images.

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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022.

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7 Key Roles and Responsibilities in Enterprise MLOps

Domino Data Lab

One of the primary challenges of any ML/AI project is transitioning it from the hands of data scientists in the develop phase of the data science lifecycle into the hands of engineers in the deploy phase. Who takes responsibility for the operationalized models? The Enterprise MLOps Process Overview.

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Customer experience examples that drive value

IBM Big Data Hub

Thankfully, customer-centric organizations have many tools, examples, and use cases at their disposal to meet the growing needs of today’s customers. It is important to meet the customer’s needs on day one, by making an emotional connection. Next, they can solicit feedback or inquire if the user needs support.