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Why ChatGPT is not an AI-Platform, but merely an AI-model?

  • nk3923
  • Jan 21
  • 2 min read

Understand what AI Platform to choose.
Use of AI-Platforms makes it easier for the end-user to use AI.

AI foundational models like Llama, Gemini and ChatGPT are large-scale models (LLMs) trained on vast datasets using self-supervised learning. These models are designed to understand and generate human-like text, images, and other data types. They serve as versatile tools that can be adapted to a wide range of tasks across various domains. For example, foundational models can be fine-tuned for specific applications like disease diagnosis in healthcare, fraud detection in finance, or customer service automation.


While foundational models present significant potential for enhancing business applications, they come with challenges related to complexity, bias, resource intensity, integration, scalability, maintenance, and user accessibility. They provide foundational capabilities but requires significant customization, integration, and technical effort to get closer on meeting specific business needs.


A true AI platform similar to ours in Pentimenti serves as a comprehensive environment that facilitates the development, deployment, and management of AI foundational models and applications. An AI platform will abstract away complex processes like prompt engineering, fine-tuning, and deployment, offering ready-to-use solutions tailored to business needs.


Its role encompasses several key functions:


  1. Integration: AI platforms integrate various foundational models, allowing developers to access and utilize these models efficiently. They provide tools and frameworks for leveraging large-scale models, such as natural language processing (NLP) and image generation models, to build applications.


  2. Training and Fine-Tuning: These platforms often include capabilities for training and fine-tuning foundational models on specific datasets. This enables organizations to adapt general-purpose models to meet their unique requirements and improve performance on targeted tasks.


  3. Scalability: AI platforms are designed to handle the considerable computational resources required for training and running foundational models. They offer scalable infrastructure, allowing organizations to manage extensive datasets and complex model architectures without significant overhead.


  4. Deployment: Once models are trained, AI platforms assist in deploying them as applications. This includes providing APIs and interfaces that enable seamless integration of AI functionalities into existing processes and everyday tasks.


  5. Monitoring and Management: AI platforms often come equipped with tools for monitoring model performance and maintaining AI applications. This includes tracking metrics, managing updates, and ensuring compliance with data governance standards.


  6. Collaboration and Experimentation: These platforms foster collaboration among data scientists, developers, and business stakeholders, enabling experimentation with different models and approaches. They often provide a user-friendly interface for non-technical users to engage with AI technologies.


In summary, an AI platform acts as a bridge between foundational models and practical AI applications, streamlining the entire lifecycle from model training to deployment and management, thereby enhancing the efficiency and effectiveness of AI initiatives within organizations.


Using Pentimenti, users can upload a document, click a button, and get an optimized bid proposal in minutes—without needing to understand AI models, prompts, or tuning. We continuously improve the underlying AI models, so users always get the most accurate and relevant outputs—no technical tweaks are needed.


You are welcome to reach out here: https://www.pentimenti.ai/bookademo for more info and a deep dive.


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