[{"data":1,"prerenderedAt":231},["ShallowReactive",2],{"project-18591065":3},{"id":4,"name":5,"line":6,"line_source":7,"title":8,"url":9,"domain":10,"maker":11,"posted_at":12,"year":13,"points":14,"comments":15,"rank_in_year":16,"shown_in_year":17,"top":18,"tier":19,"category":20,"subcategory":21,"format":22,"keywords":23,"reception":29,"sentiment":30,"stance_mix":31,"scored_comments":35,"aspects":36,"strip":41,"post_text":43,"what":44,"said":77,"maker_in_thread":121,"shape":126,"grid":130,"attempts":192,"same_week":193,"maker_shows":33,"maker_hidden":223,"reader_version":224,"card":225},"18591065","Lambdo","Feature engineering and machine learning together","title","Show HN: Lambdo – Feature engineering and machine learning together","https:\u002F\u002Fgithub.com\u002Fasavinov\u002Flambdo","github.com","asavinov",1543859921,2018,73,24,597,2155,28,"notable","ai","ml workflow engine","library",[24,25,26,27,28],"feature engineering","machine learning pipeline","model training","time series analysis","python","curious",0.167,{"positive":32,"neutral":33,"mixed":34,"critical":34},1,5,0,6,{"idea":37,"docs":38,"performance":39,"design":40},{"positive":32,"critical":34},{"positive":34,"critical":34},{"positive":34,"critical":34},{"positive":34,"critical":34},[34,34,34,34,34,34,33,42,42,42,34,42,34,34,42,34,34,34,34,34,34,34,34,34],3,null,{"problem":45,"audience":56,"pricing":43,"motivation":65,"time_to_build":43,"stack":71,"openness":76,"stage":76,"team_size":76},{"value":46,"source":47,"evidence":48},"Feature engineering and model training are separate steps that are hard to deploy together as one workflow","maker",[49,52],{"id":50,"by":11,"text":51},"18591100","Lambdo is a workflow engine which simplifies data analysis by combining in one analysis pipeline * Feature engineering and machine learning: Lambdo does not distinguish them and…",{"id":53,"by":54,"text":55},"18592019","kmax12","I definitely see the need for packages like this. So much of a data scientist's time is spent on feature engineering, but there are relatively few tools out there that are trying…",{"value":57,"source":58,"evidence":59},"Data scientists and ML practitioners","inferred",[60,61],{"id":53,"by":54,"text":55},{"id":62,"by":63,"text":64},"18593837","mooneater","> 'value', 'leverage' and 'resonate' You could not be more wrong about my perspective though. My question is from this perspective: I use tensorflow, keras, numpy, scikit-learn,…",{"value":66,"source":47,"evidence":67},"Treat feature definitions and ML models as one kind of data transformation",[68],{"id":69,"by":11,"text":70},"18592034","Because they are so much similar that it is much easier to implement them as part of one mechanism. For example: * Apply a (say, SVM) classification model to each object (row) by…",[72],{"value":73,"source":47,"evidence":74},"Python",[75],{"id":50,"by":11,"text":51},"unknown",{"praise":78,"objections":84,"requests":98,"questions":107,"alternatives":116},[79],{"value":80,"source":81,"evidence":82},"Feature engineering deployment tooling is needed","commenter",[83],{"id":53,"by":54,"text":55},[85,92],{"value":86,"source":81,"evidence":87},"Unclear why feature engineering and ML should be treated the same",[88],{"id":89,"by":90,"text":91},"18591848","pplonski86","I'm also reading the readme and trying to guess why I need this package? One more question, why treating feature engineering and ML models the same way is important?",{"value":93,"source":81,"evidence":94},"Readme lacks a small self-contained example",[95],{"id":96,"by":63,"text":97},"18591814","Things I always want when looking at something new: - Where does it sit in relation to other data science components? - What does it integrate with, and what is it agnostic to? -…",[99,103],{"value":100,"source":81,"evidence":101},"Self-contained minimal use case",[102],{"id":96,"by":63,"text":97},{"value":104,"source":81,"evidence":105},"Integration with tensorflow, keras, numpy, scikit-learn, pandas",[106],{"id":62,"by":63,"text":64},[108,112],{"value":109,"source":81,"evidence":110},"How does it sit relative to other data science components",[111],{"id":96,"by":63,"text":97},{"value":113,"source":81,"evidence":114},"Could it combine with Featuretools",[115],{"id":53,"by":54,"text":55},[117],{"value":118,"source":81,"evidence":119},"Featuretools",[120],{"id":53,"by":54,"text":55},{"comments":122,"answered":32,"key":123},2,[124,125],{"id":50,"text":51},{"id":69,"text":70},{"commenters":127,"max_depth":128,"maker_replies":122,"first_comment_mins":42,"lifespan_hours":129},11,8,71,[131,135,138,141,144,148,150,152,154,156,157,158,161,165,168,170,172,174,176,180,183,185,187,190],[132,34,34,133,134,34],"18591794","commenter\u002Fneutral\u002Fidea","ericand",[136,34,34,133,137,34],"18592880","tvladeck",[139,34,34,133,140,34],"18594919","halflings",[142,34,34,133,143,34],"18595105","jacquesm",[145,34,34,146,147,34],"18596801","commenter\u002Fneutral\u002Fnone","brylie",[50,32,34,149,11,32],"project\u002Fneutral\u002Fidea",[53,122,33,151,54,34],"project\u002Fpositive\u002Fidea",[153,122,42,149,90,34],"18592101",[96,42,42,155,63,34],"project\u002Fneutral\u002Fdocs",[89,42,42,149,90,34],[69,42,34,151,11,32],[159,42,42,160,90,34],"18592170","project\u002Fneutral\u002Fperformance",[162,42,34,163,164,34],"18592379","commenter\u002Fcritical\u002Fnone","srean",[166,42,34,167,63,34],"18593821","commenter\u002Fneutral\u002Fdocs",[62,42,42,169,63,34],"project\u002Fneutral\u002Fdesign",[171,42,34,146,164,34],"18595350",[173,42,34,146,63,34],"18611051",[175,42,34,146,164,34],"18620007",[177,42,34,178,179,34],"18594246","other\u002Fcritical\u002Fidea","mcrad",[181,42,34,182,164,34],"18595219","other\u002Fneutral\u002Fidea",[184,42,34,146,179,34],"18599471",[186,42,34,182,164,34],"18601534",[188,42,34,189,179,34],"18603013","other\u002Fneutral\u002Fnone",[191,42,34,182,164,34],"18606086",[],[194,199,204,209,214,219],{"id":195,"name":196,"points":197,"category":198},"18599200","Dark Reader extension",369,"productivity",{"id":200,"name":201,"points":202,"category":203},"18576117","dust3d.org",290,"dev_tools",{"id":205,"name":206,"points":207,"category":208},"18606766","Stock Trading from Google Spreadsheet",281,"finance_crypto",{"id":210,"name":211,"points":212,"category":213},"18569961","srslte-docker-emulated",210,"infrastructure",{"id":215,"name":216,"points":217,"category":218},"18568072","Tabulator",205,"languages",{"id":220,"name":221,"points":222,"category":203},"18608454","Next Browser",198,false,"v2",{"id":226,"name":5,"line":6,"ai":223,"cat":20,"year":13,"points":14,"comments":15,"rec":29,"s":227,"top":18,"kw":228,"maker":11,"domain":10,"tier":19,"attempts":34,"strip":229,"url":9,"mix":230,"praise":80,"objection":86,"sub":21},18591065,0.17,[24,25,26,27,28],"000000533303003000000000",[32,33,34,34],1794036572379]