Hey there! Have you ever found yourself scratching your head over unpredictable cloud data costs? It’s no secret that accurately forecasting cloud data spend can be a real headache. Fluctuating costs make it challenging to plan and allocate resources effectively, leaving businesses vulnerable to budget overruns and financial challenges. But don’t worry, we’ve got you covered!

In part one of this two part series, I reviewed the history of the chatbot, my 2003 patent, and the reasons why the conditions weren’t right for the type of chat experience we’re all now enjoying with ChatGPT. For part two, we get into what has changed and the different ways enterprises can drive modern chatbot experiences with ChatGPT.

The release of ChatGPT pushed the interest in and expectations of Large Language Model based use cases to record heights. Every company is looking to experiment, qualify and eventually release LLM based services to improve their internal operations and to level up their interactions with their users and customers. At Cloudera, we have been working with our customers to help them benefit from this new wave of innovation.

Today, organizations must do more with less. The pace of innovation has increased exponentially, yet resources remain the same (or are dwindling). Between talent shortages, long development cycles that rely on traditional programming languages, and technology teams that are already stretched perilously thin, many businesses have glaring operational problems they simply can’t solve with their current resources.

Learn the optimizations for a dbt deployment that are essential for a dependable data stack.