How to Use Feast for SLM/LLM Post-Training (with Ray) Name Type Fields web_documents FeatureView human, bot, human_repeat_ratio, bot_repeat_ratio train_example OnDemandFeatureView cleaned_human, cleaned_bot, char_count, is_trainable, sft_text llm_posttrain FeatureService web_documents + train_example Source data is prepared parquet (document_id + event_timestamp already present). No Feast core patches. Paths Flag What happens (default) to_ray_dataset() + preprocess sft_text (ODFV does not run) --via-df to_df() so ODFV train_example runs Setup uv pip install -e "../../sdk/python[ray]" -r requirements.txt PYTHONPATH=../../sdk/python python scripts/prepare_data.py cd feature_repo && feast apply && cd .. Run (data load only) PYTHONPATH=../../sdk/python python scripts/train_sft.py --dry-run PYTHONPATH=../../sdk/python python scripts/train_sft.py --dry-run --via-df Blog How to Use Feast for SLM/LLM Post-Training with Ray