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Data Science > Features
To develop and deploy effective AI systems, you need tools, techniques, and methodologies to hand data, build and evaluate models, and generate insights. Welcome to Data Science.

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The Multimodal World of Enterprise AI

Multimodal applications of AI are gaining credence throughout the enterprise at increasingly rapid rates—and for good reason. Firstly, they come close to realizing the full potential of foundation models and large language models (LLMs) which, by default, are multimodal.

AI Across Markets and Industries

With all the noise around generative AI (GenAI) since the advent of ChatGPT, has the signal gotten lost? Are we missing the one "killer app" that will make GenAI indispensable? What is more likely is that multiple killer apps will surface across many markets and industries.

Making Data More Accessible and Usable: Knowledge Graphs, Semantic Layers, and Vector Databases

The collective fields of knowledge, data, content, and information management have experienced massive changes over the last several years. AI, not long ago a concept mainly reserved for science fiction, has become an everyday tool for information workers.

Fast, Accurate, Relevant, Intuitive: The Future of Search

What attributes come to mind when you consider a great search experience? You would probably say it is fast, accurate, relevant, and intuitive. You would focus on what the act of searching allowed you to achieve.

AI Techniques Powering Enterprise Productivity: From Automation to Augmented Intelligence

AI stands beyond experimental status—it is a founding power for corporate productivity delivery. From automating repetitive workflows to enhancing complex decision making, AI transforms how work gets done.

Accelerating AI Development With Synthetic Data

Synthetic data applications are at the intersection of some of the most meaningful developments in enterprise AI. Synthetic data techniques represent some of the earliest manifestations of generative AI and predate the widespread adoption of language models. In fact, employing synthetic data is one of the foremost methods of building—and fine-tuning—language models and foundation models in general.

The Rise of GenAI and LLMs

In 1950, Alan Turing suggested a test to determine if computers could mimic human intelligence well enough that an impartial observer could no longer tell the difference. We are still talking about the Turing Test almost 75 years after its inception.

The AI-Driven Transformation of Enterprise Data Architecture

The past year has been an exhilarating one with AI and more specifically, generative AI (GenAI), quickly emerging as a transformative force, reshaping how businesses will operate, innovate, and interact with customers. As AI continues to gain prominence, its impact on enterprise data architecture is becoming increasingly apparent.

Upskilling and Reskilling in the Age of AI

The AI revolution is upon us. A tidal wave of AI technology is sweeping across businesses, fundamentally reshaping how employees, managers, and AI system developers approach their work.