{"id":"W4405092785","doi":"10.1109/ms.2024.3477014","title":"MLOps, LLMOps, FMOps, and Beyond","year":2024,"lang":"en","type":"article","venue":"IEEE Software","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Software engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000213665,0.0001010848,0.00009687431,0.0001242039,0.0001955716,0.000794266,0.0005493459,0.00004986071,0.00002570392],"category_scores_gemma":[0.00001798057,0.00008015311,0.00003637209,0.0005407271,0.00004752651,0.005469003,0.000173927,0.00009330247,0.000448815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001400366,"about_ca_system_score_gemma":0.0001005631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000068235,"about_ca_topic_score_gemma":6.044464e-7,"domain_scores_codex":[0.9990506,0.00002012158,0.0001941808,0.0002669902,0.0002614291,0.0002067382],"domain_scores_gemma":[0.9994466,0.00007425246,0.00002760326,0.0003110432,0.00002994566,0.0001105578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002139119,0.00001978555,0.002909453,0.0002695351,0.0000340841,0.00009479371,0.02636218,0.00005287103,0.0002366585,0.06600965,0.2316995,0.6723094],"study_design_scores_gemma":[0.0001682304,0.0000999073,0.001550718,0.0001152734,0.000002571684,0.0002219543,0.0002982569,0.02811178,0.001753518,0.01128089,0.9559665,0.0004304554],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0444519,0.00472439,0.9253906,0.003711942,0.006765255,0.0003053345,0.00002669385,0.002288727,0.01233517],"genre_scores_gemma":[0.9633614,0.0001356233,0.02755879,0.00365595,0.0003562933,0.00002257272,0.000008072228,0.00001511259,0.004886155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9189095,"threshold_uncertainty_score":0.7659125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057780781901161,"score_gpt":0.2222352803670211,"score_spread":0.2116574725480095,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}