Databricks-Big-Book-Of-GenAI

Databricks-Big-Book-Of-GenAI

Achieving Production-Quality GenAI Requires New Tools and Skills

Generative AI has opened new worlds of possibilities for businesses and is being emphatically embraced
across organizations. According to a recent MIT Tech Review report, all 600 CIOs surveyed stated they are
increasing their investment in AI, and 71% are planning to build their own custom large language models (LLMs)
or other GenAI models.

However, many organizations have found it challenging to deploy these applications at
production quality. To meet the standard of quality required for customer-facing applications, AI output must
be accurate, governed and safe.

Data Infrastructure Must Evolve to Support

GenAI-Powered Applications
Making the leap to generative AI is not just about deploying a chatbot; it requires a reshaping of the foundational
aspects of data management. Central to this transformation is the emergence of data lakehouses as the new
“modern data stack.” These advanced data architectures are essential to harnessing the full potential of GenAI,
driving faster, more cost-effective and wider democratization of data and AI technologies.

As businesses increasingly rely on GenAI-powered tools and applications for competitive advantage, the underlying data
infrastructure must evolve to support these advanced technologies effectively and securely.

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