This blog series demystifies generative AI (Gen AI) for corporations and technology leaders. It provides easy frameworks and guiding principles in your transformative artificial intelligence (AI) journey. In the previous blog, we discussed IBM's differentiated approach to delivering enterprise-class models. In this blog, we take a look at why base model selection is significant and the way it enables corporations to soundly scale genetic AI.
Why is model selection vital?
In the dynamic world of genetic AI, uniform approaches are insufficient. As corporations look to harness the facility of AI, it’s needed to have a big selection of model decisions to:
- Drive innovation: A various range of models not only fosters innovation by bringing distinctive strengths to handle a wide range of problems, but additionally enables teams to adapt to evolving business needs and customer expectations.
- Customize it to achieve a competitive advantage: A spread of models enable corporations to tailor AI applications to area of interest needs, thereby gaining a competitive advantage. Gen AI could be tailored to specific tasks, be it answering questions in chat applications or writing code to generate quick summaries.
- Accelerate time to market: In today's fast-paced business environment, time is of the essence. A various model portfolio can speed up the event process and enable corporations to quickly introduce AI-powered offerings. This is especially vital in genetic AI, where access to the newest innovations represents a key competitive advantage.
- Remain flexible to changes: Market conditions and business strategies are always evolving. Various model options enable corporations to change quickly and effectively. Access to multiple options means that you can quickly adapt to latest trends or strategic changes, ensuring agility and resilience.
- Optimize costs across all use cases: Different models have different cost implications. With access to a spread of models, corporations can select probably the most cost-effective option for every application. While some tasks may require the precision of pricy models, others could be solved with cheaper alternatives without sacrificing quality. For example, in customer support, throughput and latency could also be more vital than accuracy, while in resources and development, accuracy is more vital.
- Reduce risks: Relying on a single model or a limited selection could be dangerous. A various model portfolio helps mitigate concentration risks and ensures corporations remain resilient to the shortcomings or failure of a specific approach. This strategy allows risk to be spread and provides alternative solutions when challenges arise.
- Follow the regulations:The regulatory landscape for AI continues to be evolving, with ethical considerations on the forefront. Different models can have different impacts on fairness, privacy and compliance. A wide array allows corporations to navigate this complex terrain and choose models that meet legal and ethical standards.
Choosing the fitting AI models
Now that we understand the importance of model selection, how can we address the problem of selection overload when choosing the fitting model for a specific use case? We can break down this complex problem right into a series of straightforward steps you could apply today:
- Identify a transparent use case: Identify the particular needs and requirements of your small business application. This includes creating detailed prompts that consider nuances inside your industry and company to make sure the model closely aligns along with your goals.
- List all model options: Evaluate different models based on size, accuracy, latency and associated risks. This includes understanding the strengths and weaknesses of every model, resembling the trade-offs between accuracy, latency and throughput.
- Evaluate model attributes: Evaluate the appropriateness of the model size relative to your needs, considering how the dimensions of the model might affect its performance and associated risks. This step focuses on properly sizing the model to best fit the use case, as greater will not be necessarily higher. Smaller models can outperform larger models in certain domains and use cases.
- Test model options: Run tests to see whether the model performs as expected under conditions that mimic real-world scenarios. This includes using academic benchmarks and domain-specific datasets to evaluate output quality and optimizing the model, for instance through timely engineering or model tuning, to optimize its performance.
- Refine your selection based on cost and deployment requirements: After testing, refine your selection by considering aspects resembling return on investment, cost effectiveness, and the practicality of deploying the model inside your existing systems and infrastructure. Adjust the choice based on other advantages resembling lower latency or increased transparency.
- Choose the model that gives probably the most profit: Make the ultimate collection of an AI model that gives the perfect balance of performance, cost and associated risks, tailored to the particular needs of your use case.
Download our model evaluation guide
IBM Watsonx™ model library
By pursuing a multi-model strategy, the IBM watsonx library offers proprietary, open source and third-party models, as shown within the image:
This offers customers a spread of decisions and allows them to pick the model that most accurately fits their individual business, regional and risk preferences.
Additionally, watsonx enables customers to deploy models on the infrastructure of their alternative with hybrid, multicloud and on-premise options to avoid vendor lock-in and reduce total cost of ownership.
IBM® Granite™: IBM's enterprise-class base models
The properties of foundation models could be divided into three fundamental attributes. Organizations need to know that excessive emphasis on one attribute can harm the others. Balancing these attributes is vital to tailoring the model to a company's specific needs:
- Trustworthy: Models which can be clear, explainable and harmless.
- Powerful: The right level of performance for targeted business domains and use cases.
- Cost-effective: Models that supply genetic AI at a lower total cost of ownership and risk.
IBM Granite is a flagship series of enterprise models developed by IBM Research®. These models feature an optimal mix of those attributes, with an emphasis on trust and reliability, enabling corporations to reach their genetic AI initiatives. Remember that corporations cannot scale genetic AI with base models that they can not trust.
View performance benchmarks from our Granite research report
IBM watsonx offers enterprise-class AI models which can be the results of a rigorous refinement process. This process begins with a model innovation led by IBM Research that features open collaborations and training on business-relevant content in accordance with the IBM AI Ethics Code to advertise data transparency.
IBM Research has developed a command optimization technique that adds features essential for enterprise use to each IBM-developed and choose open source models. Beyond academic benchmarks, our “FM_EVAL” dataset simulates real-world AI applications in corporations. The most robust models from this pipeline are made available on IBM® watsonx.ai™, providing customers with reliable, enterprise-class AI base models of the generation, as shown within the image:
Latest model announcements:
- Granite code models: a family of models trained in 116 programming languages and ranging in size from 3 to 34 billion parameters, in each a base model and statement-following model variants.
- Granite-7b-lab: Supports common tasks and is optimized using IBM's Large-Scale Alignment of Chatbots (LAB) to include latest skills and knowledge.
Test our enterprise-class foundation models on Watsonx with our latest watsonx.ai chat demo. Discover their skills in summarizing, content creation and document processing through an easy and intuitive chat interface.
Learn more about IBM Watsonx Foundation models
