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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics.

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Companies need help making the dream of digital transformation a reality

IBM Big Data Hub

For example, when a customer contacts the business via chat, email or social media, that text or voice recording is unstructured data that needs to be collected and analyzed as part of the interaction. Dublin-based Glen Dimplex has sales, manufacturing and distribution facilities around the world.

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How foundation models and data stores unlock the business potential of generative AI

IBM Big Data Hub

Instead of spending time and effort on training a model from scratch, data scientists can use pretrained foundation models as starting points to create or customize generative AI models for a specific use case. A specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks.

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8 Best Practices for Getting the Most From Master Data Management

Reltio

The best practice to combine different types of master data goes far beyond your internal data sets. Using data to win in your market means using data that your competitors can’t access, like your business’ unique Big Data, IoT, and unstructured data in videos, chats, and audio.

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