In the previous section, we provided a high-level overview of the key features and benefits of the Openfabric AI protocol. Now, it's time to dive deeper into the topic of AI-Apps. But before we do that, it's important to understand what an AI model is.
What is an AI model?
An AI model serves as a computational representation of real-world scenarios, designed to analyze extensive datasets for predictive or action-oriented insights. Composed of a collection of algorithms and adjustable parameters, the model undergoes a refinement process known as training. Throughout the training phase, the model ingests copious amounts of data, identifying patterns and correlations among various factors. Upon completion of the training, the AI model is equipped to generate predictions or initiate actions based on novel data inputs.
Now that we have a better understanding of what an AI model is, we can delve further into the topic of AI-Apps and how they can be developed and deployed using the Openfabric AI protocol.
What is an AI-App?
Although AI models serve as sophisticated technical constituents, delivering the essence of intelligence, a multifaceted interface is required to make them accessible and functional for end-users.
AI-Apps represent a groundbreaking approach to interaction with AI models, furnishing the complete context necessary for seamless engagement, deployment, and maintenance. This method not only enables large-scale AI model management but also ensures an exceptional user experience.
What language can I use to write AI-Apps?
With Openfabric AI, you have the flexibility to develop intelligent AI-Apps in Python, utilizing your framework of choice, be it TensorFlow, PyTorch, or others.