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What is tokenization in the context of AI model training?
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Tokenization is the process of converting the prepared data pile into tokens, which can potentially be in the trillions.
What is a Foundation Model in AI?
A Foundation Model is a focused, centralized base model that can be adapted to specialized applications through fine-tuning.
What are some example applications of Foundation Models?
Examples include programming language translation and customer service chatbots.
How does the use of Foundation Models impact the development of specialized AI models?
They allow for increased sophistication and a more rapid development cycle for AI and AI-derived applications.
Why is extensive computational resources and time required during the training process of AI models?
The training process involves processing large volumes of tokenized data, leading to high computational and time demands.
Who typically engages in the tuning stage of Foundation Model development and what do they do?
Application developers, who generate prompts for performance and provide additional local data to fine-tune the model.
What is the advantage of using Foundation Models in AI development?
They rapidly speed up AI model development by allowing specialized models to be created through fine-tuning rather than from scratch.
What types of data are used in the data preparation stage for AI model training?
A combination of open-source data and proprietary data.
What is the purpose of benchmarking in the validation stage of AI model development?
Benchmarking assesses the model's performance against set standards to gauge effectiveness.
How does watsonx.ai support AI model development?
It allows application developers to engage with and fine-tune models.
Upon what platform is IBM’s watsonx built?
IBM’s watsonx is built on the Red Hat OpenShift hybrid cloud platform.
What is the purpose of the watsonx.data component in IBM's watsonx platform?
It functions as a modern data lakehouse, connecting various data repositories.
Name three data processing tasks involved in preparing data for training an AI model.
Categorization, filtering, and removing duplicates.
What are the two primary deployment options for AI models?
Public cloud deployment as a service offering, and embedded application deployment closer to the network edge.
What is a model card and what information does it typically include?
A model card documents the training process and benchmark scores of an AI model, and is primarily used by data scientists.
What role does watsonx.governance play in the IBM watsonx platform?
It manages data and model cards and ensures governance of the AI process.
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