Overall, the GreenNode AI Platform forms an end-to-end pipeline for building, training, managing, and deploying machine learning models in an AI platform, which includes four main components Notebook Instance, Model Training, Model Registry and Online Prediction. These components are fundamental and interrelated, each serving a specific purpose within the AI/ML workflow.

Components

• Notebook: This is an interactive environment where data scientists and engineers can write, execute, and iterate on code. It usually includes features for data exploration, visualization, and collaborative work. Notebooks often support multiple programming languages like Python, R, or Scala and facilitate the development and testing of algorithms and models.

• Model Training: Model training involves using datasets to create and optimize machine learning models. This process occurs in the cloud environment, where data scientists run code to build models and tune hyperparameters. Training uses computational resources like GPUs or TPUs to accelerate the learning process and improve model accuracy.

• Model Registry: The model registry is a centralized repository for storing trained models, their metadata, versions, and associated artifacts. It allows for version control, management, and organization of models developed during the training phase. This enables easy access, retrieval, and deployment of models for various purposes.

• Online Prediction: After training and registering the model, the online prediction component enables the deployment and serving of models to make real-time predictions or inferences on new data. This component provides endpoints or APIs that can be integrated into applications, allowing them to leverage the trained models to process new data and generate predictions.
  

• Network Volume: Network Volumes, powered by Vast Storage on the Greennode AI Platform, represent a high-performance, scalable, and cost-effective solution for storing and managing large datasets commonly found in AI projects. By leveraging the capabilities of Vast Storage, Network Volumes offer exceptional performance, durability, and flexibility, making them an ideal choice for demanding AI workloads.

• Notebooks are for coding and experimentation.

• Model Training refines and creates the machine learning models.

• Model Registry manages and organizes models.

• Online Prediction serves the deployed models for real-time predictions.
  

• Network Volume stores the associated big data sets.

• The Notebook is where the data preprocessing, model training, and evaluation code are written.

• During the training phase in the Notebook, models are developed and optimized.

• Once a model is trained and finalized, it's stored in the Model Registry.

• The Online Prediction component then accesses the Model Registry to deploy and serve the models for real-time predictions or inferences.
  

• Network Volume performs as a storage where you can store and access the related data.