Quickly build a practical understanding of generative AI with DataRobot. Our guide shares essential foundational knowledge for understanding and developing GenAI solutions, including the different types of foundation models, how foundation models can be used in business, assessing GenAI fit for specific use cases, and managing risk.
The guide also shares how to increase the efficiency of data science teams and improve the accuracy of GenAI apps with DataRobot’s OpenAI integration. Learn proven best practices for using LLMs, deepen your generative AI expertise, and start delivering GenAI apps to your business with this comprehensive guide.
Shorten your path to GenAI impact with answers to your critical questions:
- What are generative AI, foundation models, and LLMs?
- How can foundation models be used in business?
- What are the risks and concerns associated with the use of foundation models?
- How does DataRobot integrate with Azure OpenAI LLMs?
Plus best practices on the use of LLMs.
DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively.
The generative AI space is changing quickly, and the flexibility, safety and security of DataRobot helps us stay on the cutting edge with a HIPAA-compliant environment we trust to uphold critical health data protection standards. We’re harnessing innovation for real-world applications, giving us the ability to transform patient care and improve operations and efficiency with confidence
DataRobot provides us with innovative ways to test new ideas. Given a problem and a dataset, DataRobot allows us to generate multiple prototypes 20% faster. And the process facilitates the learning evolution of our data scientists.
The value of having a single platform that pulls all the components together can’t be underestimated. Then there’s the combination of the technology and the collaborative DataRobot team. If either one of those wasn’t there, I would have looked elsewhere.