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AI Training & AI Inference Explained

AI Training Flow

  • AI Training builds and refines the model’s intelligence through data collection, algorithm selection, optimization, and evaluation.
  • Deployment makes the trained model available for real-world use within an AI application.
  • AI Inference applies the model’s learned knowledge to user inputs(prompts) and generates meaningful outputs in real time.
  • AI applications connect end users to the model, delivering responses powered by the inference process.
  • GPUs accelerate both training(data processing, optimization, evaluation) and inference(fast, real-time predictions)

End-to-End AI Training Architecture

  • AI training is a step-by-step process that transforms raw data into a usable model
  • Overall flow: collect → prepare → train → evaluate → package & store

AI Training Flow

Training Flow Overview

  • Data is collected from storage, logs, or databases
  • The data is cleaned and prepared for training
  • GPU servers train the model using the processed data
  • The model is evaluated for accuracy and quality
  • The final model is packaged and saved in a registry

Data Ingestion and Preparation

  • Data sources include object storage, databases, log files, and CSVs
  • Data cleaning fixes missing values, errors, and inconsistent formats
  • Text is tokenized, images are resized, numeric values are normalized
  • Labels are added for supervised learning tasks
  • Common tools: Pandas, Spark, Airflow, HuggingFace Datasets

GPU Training Architecture

  • Training uses GPU servers because they handle large matrix operations efficiently
  • Training can run on a single GPU or scale across multiple GPUs
  • Checkpoints are saved so progress isn’t lost
  • Jobs can be automated through workflow tools
  • Typical frameworks:
    • PyTorch
    • TensorFlow
    • Kubeflow
    • Ray AI Training Flow

Model Evaluation

  • The model is tested with unseen data
  • Key checks include:
  • accuracy
  • error rate
  • speed
  • reliability
  • For LLMs, evaluation also checks safety, consistency, and hallucination
  • Evaluation results help determine whether the model is ready or needs improvement

Model Packaging and Registry

  • The trained model is exported into an inference-ready format
  • Supporting files (tokenizers, configs) are saved together
  • The model is stored in a registry for version tracking
  • Example: MLflow, HuggingFace Hub, S3 folders
  • Versioning allows easy deployment, rollback, and collaboration

HuggingFace UI

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