Spring Forward With New AI Tips and Tools

Perhaps the birds chirping, allergies acting up, and the flowers blossoming are just the regular rigamarole of spring, but there are many other things to get hyped about this April, particularly when it comes to AI, data science, and machine learning.

For starters, have you been hearing the buzz about ChatGPT but still aren't sure what it means for your organization? Take a look at our new, easy-to-read guide where you can explore how large language models can be used in the enterprise. On top of that, explore an ebook for finance teams looking to tackle challenges to advanced analytics, detailed technical articles, and more.

The?Dataiku Digest?provides you with all of the new and relevant information that you're looking for, so don't forget to subscribe!

Major Marketing AI Concepts???

Kicking off our revamped “In Plain English” blog series, this blog provides an easily digestible understanding of key marketing AI concepts. Plus, it includes concrete examples of how this topic ties into common use cases and challenges.

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Using LLMs in the Enterprise???

ChatGPT, Bing, and Bard are all products built on top of a class of technologies called Large Language Learning Models (LLMs). Get a deeper understanding and discover how these products can be leveraged best in business in this quick rundown.?

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Dataiku for Business Analysts???????

This video points out all of the features of Dataiku that business analysts will find especially valuable for their specific responsibilities. Follow the walkthrough of all the different ways that business analysts can leverage Dataiku to make their lives easier and their insights richer.?

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Advancing Analytics in Finance Teams???

There could be a number of things lurking that are holding your team back from its full potential: fragile reporting processes, a continuous list of ad-hoc requests, a lack of data lineage, etc. In this ebook, figure out how to get to the root of these issues and transform your finance team.?

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Working Without Training Data??

For a long time, having a significant amount of training data has been a prerequisite for successful ML projects. However, new state-of-the-art approaches actually enable you to?mitigate and even overcome this constraint in the context of computer vision. Learn more about how these approaches work in this blog post.

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