PrecisionAI

Release: Beta — core workflows available; some capabilities still maturing

ML training-data precision optimization

PrecisionAI right-sizes training data precision to match what models actually use. It profiles datasets to determine meaningful per-column precision, then emits YAML manifests that PyTorch, TensorFlow, and Spark DataLoaders consume — reducing storage, load costs, and memory bandwidth by optimizing the data layer rather than the model.

The profile_training_data() API ships today; DataLoader integration is on the roadmap.

What it does

Training pipelines load FP64 data from disk, then the first GPU operation casts to FP16/BF16. Fifty to seventy-five percent of every load is wasted precision nobody uses. Model quantization (GPTQ, AWQ, bitsandbytes) optimizes the model after training. PrecisionAI optimizes the data before training — profiling each column, emitting a manifest, and letting DataLoaders right-size on read.

The profile_training_data() API ships today; PyTorch/TF DataLoader wrappers are on the roadmap.

When to use it

ML infrastructure teams paying for GPU hours and storage on large FP64 training sets.

See Examples for runnable code.

Who it is for

• ML infrastructure teams optimizing GPU compute and data loading
• Feature store engineers managing large FP64 training datasets
• DoD CDAO and national lab AI/ML programs

What ships

• Python wheel with licensing (precisionai)
• profile_training_data() and manifest emission
• Guided journeys 01→06 under python/examples/journeys/