{"id":"W4410381405","doi":"10.48175/ijarsct-19200a","title":"Optimized Deployment of Multi-Objective Machine Learning Models in Azure ML: A Compliance-Driven and Cost-Conscious Pipeline Framework","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Science Communication and Technology","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"ASTER","funders":"","keywords":"Software deployment; Pipeline (software); Compliance (psychology); Computer science; Artificial intelligence; Machine learning; Software engineering; Operating system; Psychology; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006072564,0.001133544,0.0007554276,0.000947058,0.0007321352,0.002962847,0.003008405,0.001188945,0.003024989],"category_scores_gemma":[0.01647466,0.0009585497,0.001136913,0.0006012102,0.001363529,0.005039373,0.004206921,0.002847863,0.001101383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001888278,"about_ca_system_score_gemma":0.003349454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006294395,"about_ca_topic_score_gemma":0.007467379,"domain_scores_codex":[0.9965581,0.001091775,0.0003023699,0.0006923532,0.0009897125,0.0003656284],"domain_scores_gemma":[0.9927787,0.002330539,0.0007402593,0.00273527,0.001056517,0.0003587021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008981379,0.0004543561,0.01265099,0.0002394586,0.0002245052,0.0004289832,0.0005686666,0.6574858,0.02181418,0.05459834,0.01307588,0.2375607],"study_design_scores_gemma":[0.00001909925,0.00005574776,0.0005392669,0.00001378053,0.00001215176,0.00002586106,0.00002167463,0.9831685,0.005216702,0.008861449,0.002046729,0.00001907701],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02568823,0.0001357586,0.9460719,0.0005486914,0.00004035198,0.0001769854,0.000226646,0.02522969,0.001881842],"genre_scores_gemma":[0.4279742,0.0001626233,0.5658046,0.0003941412,0.00004220365,0.0002691057,0.001018398,0.001906519,0.002428184],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006294395,"threshold_uncertainty_score":0.03211516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2636806500567445,"score_gpt":0.514017029646205,"score_spread":0.2503363795894605,"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."}}