Artificial Intelligence (AI) has revolutionized the development of specialized models such as customer service chatbots and fraud detection systems. In the past, creating a new AI model required starting from scratch – gathering and curating data, labeling, model development, training, and validation. However, the advent of foundation models has transformed this process. So, what exactly is a foundation model? In simple terms, it is a base model that can be adapted to create specialized models through fine-tuning. Let’s explore the five stages of building an AI model.
Contents
Stage 1: Prepare the Data
The first stage involves preparing the data to train our AI model. This requires a significant amount of data, potentially petabytes, across various domains. The data can include both open-source and proprietary data. In this stage, we perform data processing tasks such as categorization, filtering, and removing duplicate data. Categorization helps identify the different types of data, such as English or German, Ansible or Java. Filtering allows us to remove unwanted content, such as hate speech or copyrighted material. After this stage, we have a base data pile, which can be versioned and tagged for governance purposes.
Stage 2: Train the Model
Once the data is prepared, we move on to training the model. We select a foundational model that aligns with our specific use case, such as a chatbot or classifier. Foundation models work with tokens rather than words, so we tokenize the data pile, which can result in trillions of tokens. Training the model can be a time-consuming process, especially for large-scale models that may require months and thousands of GPUs. However, once the training is complete, the most significant computational costs are behind us.
Stage 3: Validate
After training the model, we need to validate its performance. This involves benchmarking the model against a set of predefined benchmarks to assess its quality. The results of the benchmarks can be used to create a model card that showcases the model’s achievements. Up until this stage, the main persona involved in the process is the data scientist.
Stage 4: Tune
In the tuning stage, we introduce the persona of the application developer. Unlike the data scientist, the application developer does not need to be an AI expert. Their role is to engage with the model and provide additional local data to fine-tune its performance. This stage can be completed in a relatively short time, allowing for quick iterations and improvements.
Stage 5: Deploy the Model
The final stage is deploying the model. The model can be deployed as a service offering in the public cloud or embedded directly into an application at the network edge. IBM, for example, offers the Watsonx platform, which encompasses all five stages of the workflow. Watsonx provides modern data lakehouse capabilities, data and model governance, and tools for application developers to engage with the model.
FAQs
Q: What is a foundation model?
A: A foundation model is a base model that can be adapted to create specialized AI models through fine-tuning.
Q: How long does training a large-scale foundation model take?
A: Training large-scale foundation models can take months, depending on the size of the model and the available computational resources.
Q: Can the model be improved over time?
A: Yes, the model can be iterated upon and improved over time, allowing for continuous enhancements and refinements.
Conclusion
Foundation models have revolutionized the way we build specialized AI models. By following the five stages of the workflow – preparing the data, training the model, validating its performance, tuning for better results, and deploying the model – teams can create AI models with greater sophistication and significantly reduce development time. To learn more about AI model development and the Watsonx platform, visit Techal.
