This blog series demystifies enterprise generative AI (gen AI) for business and technology leaders. It provides simple frameworks and guiding principles for your transformative artificial intelligence (AI) journey. In the previous blog, we discussed the differentiated approach by IBM to delivering enterprise-grade models. In this blog, we delve into why foundation model choices matter and how they empower businesses to scale gen AI with confidence.
In the dynamic world of gen AI, one-size-fits-all approaches are inadequate. As businesses strive to harness the power of AI, having a spectrum of model choices at their disposal is necessary to:
Now that we understand the importance of model selection, how do we address the choice overload problem when selecting the right model for a specific use case? We can break down this complex problem into a set of simple steps that you can apply today:
By pursuing a multimodel strategy, the IBM watsonx library offers proprietary, open source and third-party models, as shown in the image:
This provides clients with a range of choices, allowing them to select the model that best fits their unique business, regional and risk preferences.
Also, watsonx enables clients to deploy models on the infrastructure of their choice, with hybrid, multicloud and on-premises options, to avoid vendor lock-in and reduce the total cost of ownership.
The characteristics of foundation models can be grouped into 3 main attributes. Organizations must understand that overly emphasizing one attribute might compromise the others. Balancing these attributes is key to customize the model for an organization’s specific needs:
IBM Granite is a flagship series of enterprise-grade models developed by IBM Research®. These models feature an optimal mix of these attributes, with a focus on trust and reliability, enabling businesses to succeed in their gen AI initiatives. Remember, businesses cannot scale gen AI with foundation models they cannot trust.
IBM watsonx offers enterprise-grade AI models resulting from a rigorous refinement process. This process begins with model innovation led by IBM Research, involving open collaborations and training on enterprise-relevant content under the IBM AI Ethics Code to promote data transparency.
IBM Research has developed an instruction-tuning technique that enhances both IBM-developed and select open-source models with capabilities essential for enterprise use. Beyond academic benchmarks, our ‘FM_EVAL’ data set simulates real-world enterprise AI applications. The most robust models from this pipeline are made available on IBM® watsonx.ai™, providing clients with reliable, enterprise-grade gen AI foundation models, as shown in the image:
Try our enterprise-grade foundation models on watsonx with our new watsonx.ai chat demo. Discover their capabilities in summarization, content generation and document processing through a simple and intuitive chat interface.