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Building better AI starts with better data, and organizations wanting to jump into AI need to take their data seriously.

Data-centric AI might feel new in a whirlwind of advancements in the last year, but startups like Snorkel and data platforms like Snowflake are here to enable it, and companies looking to make long-term, value-focused AI plays.

What is Data-Centric AI?

It has been said that 80% of a data scientist’s time is spent working on data. Data-centric AI focuses on the patterns, and technologies used when improving AI.

This can mean outlier detection, programmatic data labeling, label fidelity, or assessing bias in datasets used in ML training. At the highest level, data-centric AI is about shifting focus from developing ever more sophisticated models to developing ever better datasets to train with.

How Data-Centric AI Meets the ChatGPT Moment

The maturation of GPT-3 into ChatGPT is the direct result of data curation and dataset construction. First sifting through vast quantities of textual data for conversations, and second, working with an army of human curators that ranked model outputs for RLHF.

With data-centric AI, these tasks are greatly simplified, and organizations can unlock their own datasets necessary to power advanced LLM use cases requiring fine-tuning.

Data-centric AI doesn’t just mean better chatbots.

Traditional ML approaches are often hamstrung by a lack of quality data as well. Generative AI is certainly exciting, but many predictive AI use cases (like multi-label classification) become possible when organizations first leverage data-centric AI techniques to build and curate critical datasets for each use case.

Do you need help turning your AI use cases into reality? Attend one of our free AI workshops today to start unlocking the untapped potential of data-centric AI! 

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