How to Create an AI Training Pipeline
Set up an end-to-end pipeline for training, evaluating, and deploying custom AI models with reproducible experiments.
What You'll Learn
This advanced-level guide walks you through how to create an ai training pipeline step by step. Estimated time: 20 min.
Step 1: Set up experiment tracking
Configure Weights and Biases or MLflow to track hyperparameters, metrics, and model artifacts for every training run.
Step 2: Build data preprocessing
Create a data pipeline that cleans, tokenizes, and formats your training data with proper train/validation/test splits.
Step 3: Configure distributed training
Set up multi-GPU training with DeepSpeed or FSDP for efficient training of large models across multiple machines.
Step 4: Implement evaluation suite
Build automated evaluation benchmarks that run after each training run to measure quality, safety, and regression.
Step 5: Automate the pipeline
Connect all stages with orchestration tools like Airflow or Prefect so training runs are fully reproducible and automated.
Frequently Asked Questions
How much compute do I need for training?▾
Fine-tuning a 7B model with LoRA needs 1 GPU for a few hours. Full fine-tuning of larger models requires 4-8 GPUs for days. Use cloud spot instances to reduce costs.
How do I manage training data quality?▾
Implement data validation checks, deduplication, quality scoring, and human review sampling. Bad data is the top cause of poor model performance.
What if my training run diverges?▾
Monitor loss curves in real-time and set up automatic early stopping. Common fixes include lowering learning rate, increasing warmup steps, and checking data quality.