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  • FOR THINGS LIKE CALCULATING THE

  • FOR THINGS LIKE CALCULATING THE

  • FOR THINGS LIKE CALCULATING THE MEDIAN FOR STANDARD DEVIATION

  • MEDIAN FOR STANDARD DEVIATION

  • MEDIAN FOR STANDARD DEVIATION OF A FEATURE, NUMBERS THAT ARE

  • OF A FEATURE, NUMBERS THAT ARE

  • OF A FEATURE, NUMBERS THAT ARE THE SAME FOR ALL EXAMPLES,

  • THE SAME FOR ALL EXAMPLES,

  • THE SAME FOR ALL EXAMPLES, TRANSFORM WILL OUTPUT A

  • TRANSFORM WILL OUTPUT A

  • TRANSFORM WILL OUTPUT A CONSTANT.

  • CONSTANT.

  • CONSTANT. FOR THINGS LIKE NORMALIZING A

  • FOR THINGS LIKE NORMALIZING A

  • FOR THINGS LIKE NORMALIZING A VALUE, VALUES WHICH WILL BE

  • VALUE, VALUES WHICH WILL BE

  • VALUE, VALUES WHICH WILL BE DIFFERENT FOR DIFFERENT

  • DIFFERENT FOR DIFFERENT

  • DIFFERENT FOR DIFFERENT EXAMPLES, TRANSFORM WILL OUTPUT

  • EXAMPLES, TRANSFORM WILL OUTPUT

  • EXAMPLES, TRANSFORM WILL OUTPUT TENSORFLOW OPS, IT WILL THEN

  • TENSORFLOW OPS, IT WILL THEN

  • TENSORFLOW OPS, IT WILL THEN PUT AN OUTFLOW GRAPH WITH THE

  • PUT AN OUTFLOW GRAPH WITH THE

  • PUT AN OUTFLOW GRAPH WITH THE CONSTANTS AND OPS.

  • CONSTANTS AND OPS.

  • CONSTANTS AND OPS. THAT IS HER MEDIC.

  • THAT IS HER MEDIC.

  • THAT IS HER MEDIC. IT CONTAINS ALL THE INFORMATION

  • IT CONTAINS ALL THE INFORMATION

  • IT CONTAINS ALL THE INFORMATION YOU NEED TO APPLY THOSE

  • YOU NEED TO APPLY THOSE

  • YOU NEED TO APPLY THOSE TRANSFORMATIONS AND FORM THE

  • TRANSFORMATIONS AND FORM THE

  • TRANSFORMATIONS AND FORM THE INPUT STAGE FOR YOUR MODEL.

  • INPUT STAGE FOR YOUR MODEL.

  • INPUT STAGE FOR YOUR MODEL. THAT MEANS THAT THE SAME

  • THAT MEANS THAT THE SAME

  • THAT MEANS THAT THE SAME TRANSFORMATIONS ARE APPLIED

  • TRANSFORMATIONS ARE APPLIED

  • TRANSFORMATIONS ARE APPLIED CONSISTENTLY BETWEEN TRAINING

  • CONSISTENTLY BETWEEN TRAINING

  • CONSISTENTLY BETWEEN TRAINING AND SERVING, WHICH ELIMINATES

  • AND SERVING, WHICH ELIMINATES

  • AND SERVING, WHICH ELIMINATES TRAINING/SERVING SKEW.

  • TRAINING/SERVING SKEW.

  • TRAINING/SERVING SKEW. IF INSTEAD YOU ARE MOVING YOUR

  • IF INSTEAD YOU ARE MOVING YOUR

  • IF INSTEAD YOU ARE MOVING YOUR MODEL FROM A TRAINING

  • MODEL FROM A TRAINING

  • MODEL FROM A TRAINING ENVIRONMENT INTO A SERVING

  • ENVIRONMENT INTO A SERVING

  • ENVIRONMENT INTO A SERVING ENVIRONMENT OR APPLICATION AND

  • ENVIRONMENT OR APPLICATION AND

  • ENVIRONMENT OR APPLICATION AND TRYING TO APPLY THE SAME

  • TRYING TO APPLY THE SAME

  • TRYING TO APPLY THE SAME FEATURE ENGINEERING IN BOTH

  • FEATURE ENGINEERING IN BOTH

  • FEATURE ENGINEERING IN BOTH PLACES, YOU HOPE THAT THE

  • PLACES, YOU HOPE THAT THE

  • PLACES, YOU HOPE THAT THE TRANSFORMATIONS ARE THE SAME

  • TRANSFORMATIONS ARE THE SAME

  • TRANSFORMATIONS ARE THE SAME BUT SOMETIMES YOU FIND THAT

  • BUT SOMETIMES YOU FIND THAT

  • BUT SOMETIMES YOU FIND THAT THEY'RE NOT.

  • THEY'RE NOT.

  • THEY'RE NOT. WE CALL THAT TRAINING SERVING

  • WE CALL THAT TRAINING SERVING

  • WE CALL THAT TRAINING SERVING SKEW AND TRANSFORM ELIMINATES

  • SKEW AND TRANSFORM ELIMINATES

  • SKEW AND TRANSFORM ELIMINATES IT BY USING EXACTLY THE SAME

  • IT BY USING EXACTLY THE SAME

  • IT BY USING EXACTLY THE SAME CODE ANYWHERE YOU RUN YOUR

  • CODE ANYWHERE YOU RUN YOUR

  • CODE ANYWHERE YOU RUN YOUR MODEL.

  • MODEL.

  • MODEL. NOW WE'RE FINALLY READY TO

  • NOW WE'RE FINALLY READY TO

  • NOW WE'RE FINALLY READY TO TRAIN OUR MODEL.

  • TRAIN OUR MODEL.

  • TRAIN OUR MODEL. THE PART OF THE PROCESS THAT

  • THE PART OF THE PROCESS THAT

  • THE PART OF THE PROCESS THAT YOU OFTEN THINK ABOUT WHEN YOU

  • YOU OFTEN THINK ABOUT WHEN YOU

  • YOU OFTEN THINK ABOUT WHEN YOU THINK ABOUT MACHINE LEARNING.

  • THINK ABOUT MACHINE LEARNING.

  • THINK ABOUT MACHINE LEARNING. TRAINER TAKES IN THE TRANSFORM

  • TRAINER TAKES IN THE TRANSFORM

  • TRAINER TAKES IN THE TRANSFORM GRAPH AND DATA FROM TRANSFORM

  • GRAPH AND DATA FROM TRANSFORM

  • GRAPH AND DATA FROM TRANSFORM AND ESCHEMA FROM SCHEMA GEN AND

  • AND ESCHEMA FROM SCHEMA GEN AND

  • AND ESCHEMA FROM SCHEMA GEN AND TRAINS THE MODEL USING YOUR

  • TRAINS THE MODEL USING YOUR

  • TRAINS THE MODEL USING YOUR MODELLING CODE.

  • MODELLING CODE.

  • MODELLING CODE. NORMAL MODEL TRAINING.

  • NORMAL MODEL TRAINING.

  • NORMAL MODEL TRAINING. BUT WHEN TRAINING IS COMPLETE,

  • BUT WHEN TRAINING IS COMPLETE,

  • BUT WHEN TRAINING IS COMPLETE, TRAINER WILL SAVE TWO DIFFERENT

  • TRAINER WILL SAVE TWO DIFFERENT

  • TRAINER WILL SAVE TWO DIFFERENT SAVED MODELS.

  • SAVED MODELS.

  • SAVED MODELS. ONE IS A NORMAL SAVE MODEL THAT

  • ONE IS A NORMAL SAVE MODEL THAT

  • ONE IS A NORMAL SAVE MODEL THAT WILL BE DEPLOYED TO PRODUCTION.

  • WILL BE DEPLOYED TO PRODUCTION.

  • WILL BE DEPLOYED TO PRODUCTION. AND THE OTHER IS AN EVAL SAVE

  • AND THE OTHER IS AN EVAL SAVE

  • AND THE OTHER IS AN EVAL SAVE MODEL THAT WILL BE USED FOR

  • MODEL THAT WILL BE USED FOR

  • MODEL THAT WILL BE USED FOR ANALYZING THE PERFORMANCE OF

  • ANALYZING THE PERFORMANCE OF

  • ANALYZING THE PERFORMANCE OF YOUR MODEL.

  • YOUR MODEL.

  • YOUR MODEL. THE CONFIGURATION FOR TRAINER

  • THE CONFIGURATION FOR TRAINER

  • THE CONFIGURATION FOR TRAINER IS WHAT YOU WOULD EXPECT.

  • IS WHAT YOU WOULD EXPECT.

  • IS WHAT YOU WOULD EXPECT. THINGS LIKE THE NUMBER OF STEPS

  • THINGS LIKE THE NUMBER OF STEPS

  • THINGS LIKE THE NUMBER OF STEPS AND WHETHER OR NOT TO USE WARM

  • AND WHETHER OR NOT TO USE WARM

  • AND WHETHER OR NOT TO USE WARM STARTING.

  • STARTING.

  • STARTING. THE CODE THAT YOU CREATE FOR

  • THE CODE THAT YOU CREATE FOR

  • THE CODE THAT YOU CREATE FOR TRAINER IS YOUR MODELING CODE.

  • TRAINER IS YOUR MODELING CODE.

  • TRAINER IS YOUR MODELING CODE. SO IT CAN BE AS SIMPLE OR

  • SO IT CAN BE AS SIMPLE OR

  • SO IT CAN BE AS SIMPLE OR COMPLEX AS YOU NEED IT TO BE.

  • COMPLEX AS YOU NEED IT TO BE.

  • COMPLEX AS YOU NEED IT TO BE. TO MONITOR AND ANALYZE THE

  • TO MONITOR AND ANALYZE THE

  • TO MONITOR AND ANALYZE THE TRAINING PROCESS YOU CAN USE

  • TRAINING PROCESS YOU CAN USE

  • TRAINING PROCESS YOU CAN USE TENSOR BOARD JUST LIKE YOU

  • TENSOR BOARD JUST LIKE YOU

  • TENSOR BOARD JUST LIKE YOU WOULD NORMALLY.

  • WOULD NORMALLY.

  • WOULD NORMALLY. IN THIS CASE, YOU CAN LOOK AT

  • IN THIS CASE, YOU CAN LOOK AT

  • IN THIS CASE, YOU CAN LOOK AT THE CURRENT MODEL TRAINING RUN

  • THE CURRENT MODEL TRAINING RUN

  • THE CURRENT MODEL TRAINING RUN OR COMPARE THE RESULTS FROM

  • OR COMPARE THE RESULTS FROM

  • OR COMPARE THE RESULTS FROM MULTIPLE MODEL TRAINING RUNS.

  • MULTIPLE MODEL TRAINING RUNS.

  • MULTIPLE MODEL TRAINING RUNS. THIS IS ONLY POSSIBLE BECAUSE

  • THIS IS ONLY POSSIBLE BECAUSE

  • THIS IS ONLY POSSIBLE BECAUSE OF THE ML METADATA STORE THAT

  • OF THE ML METADATA STORE THAT

  • OF THE ML METADATA STORE THAT WE TALKED ABOUT IN OUR LAST

  • WE TALKED ABOUT IN OUR LAST

  • WE TALKED ABOUT IN OUR LAST EPISODE.

  • EPISODE.

  • EPISODE. TFX MAKES IT FAIRLY EASY TO DO

  • TFX MAKES IT FAIRLY EASY TO DO

  • TFX MAKES IT FAIRLY EASY TO DO THIS KIND OF COMPARISON WHICH

  • THIS KIND OF COMPARISON WHICH

  • THIS KIND OF COMPARISON WHICH IS OFTEN REVEALING.

  • IS OFTEN REVEALING.

  • IS OFTEN REVEALING. NOW THAT WE'VE TRAINED OUR

  • NOW THAT WE'VE TRAINED OUR

  • NOW THAT WE'VE TRAINED OUR MODEL, HOW DO THE RESULTS LOOK?

  • MODEL, HOW DO THE RESULTS LOOK?

  • MODEL, HOW DO THE RESULTS LOOK? THE EVALUATOR COMPONENT WILL

  • THE EVALUATOR COMPONENT WILL

  • THE EVALUATOR COMPONENT WILL TAKE THE MODEL TRAINER CREATED

  • TAKE THE MODEL TRAINER CREATED

  • TAKE THE MODEL TRAINER CREATED AND USE DEEP ANALYSIS USING

  • AND USE DEEP ANALYSIS USING

  • AND USE DEEP ANALYSIS USING BEAM AND THE MODEL ANALYSIS

  • BEAM AND THE MODEL ANALYSIS

  • BEAM AND THE MODEL ANALYSIS LIBRARY.

  • LIBRARY.

  • LIBRARY. NOT JUST LOOKING AT THE TOP

  • NOT JUST LOOKING AT THE TOP

  • NOT JUST LOOKING AT THE TOP LEVEL RESULTS ACROSS THE WHOLE

  • LEVEL RESULTS ACROSS THE WHOLE

  • LEVEL RESULTS ACROSS THE WHOLE DATASET, IT IS LOOKING DEEPER

  • DATASET, IT IS LOOKING DEEPER

  • DATASET, IT IS LOOKING DEEPER THAN THAT.

  • THAN THAT.

  • THAN THAT. AT INDIVIDUAL SLICES OF OUR

  • AT INDIVIDUAL SLICES OF OUR

  • AT INDIVIDUAL SLICES OF OUR DATASET.

  • DATASET.

  • DATASET. THAT'S IMPORTANT BECAUSE THE

  • THAT'S IMPORTANT BECAUSE THE

  • THAT'S IMPORTANT BECAUSE THE EXPERIENCE THAT EACH USER OF

  • EXPERIENCE THAT EACH USER OF

  • EXPERIENCE THAT EACH USER OF OUR MODEL HAS WILL DEPEND ON

  • OUR MODEL HAS WILL DEPEND ON

  • OUR MODEL HAS WILL DEPEND ON THEIR INDIVIDUAL DATA POINT.

  • THEIR INDIVIDUAL DATA POINT.

  • THEIR INDIVIDUAL DATA POINT. OUR MODEL MAY DO WELL OVER OUR

  • OUR MODEL MAY DO WELL OVER OUR

  • OUR MODEL MAY DO WELL OVER OUR ENTIRE DATASET, BUT IF IT DOES

  • ENTIRE DATASET, BUT IF IT DOES

  • ENTIRE DATASET, BUT IF IT DOES POORLY ON A DATA POINT THAT A

  • POORLY ON A DATA POINT THAT A

  • POORLY ON A DATA POINT THAT A USER GIVES IT.

  • USER GIVES IT.

  • USER GIVES IT. THAT USER'S EXPERIENCE IS POOR.

  • THAT USER'S EXPERIENCE IS POOR.

  • THAT USER'S EXPERIENCE IS POOR. WE'LL TALK ABOUT THIS MORE IN

  • WE'LL TALK ABOUT THIS MORE IN

  • WE'LL TALK ABOUT THIS MORE IN OUR NEXT EPISODE.

  • OUR NEXT EPISODE.

  • OUR NEXT EPISODE. SO NOW THAT WE'VE LOOKED AT OUR

  • SO NOW THAT WE'VE LOOKED AT OUR

  • SO NOW THAT WE'VE LOOKED AT OUR MODEL'S PERFORMANCE, SHOULD WE

  • MODEL'S PERFORMANCE, SHOULD WE

  • MODEL'S PERFORMANCE, SHOULD WE PUSH IT TO PRODUCTION?

  • PUSH IT TO PRODUCTION?

  • PUSH IT TO PRODUCTION? IS IT BETTER OR WORSE THAN WHAT

  • IS IT BETTER OR WORSE THAN WHAT

  • IS IT BETTER OR WORSE THAN WHAT WE ALREADY HAVE IN PRODUCTION?

  • WE ALREADY HAVE IN PRODUCTION?

  • WE ALREADY HAVE IN PRODUCTION? WE DON'T WANT TO PUSH A WORSE

  • WE DON'T WANT TO PUSH A WORSE

  • WE DON'T WANT TO PUSH A WORSE MODEL JUST BECAUSE IT'S NEW.

  • MODEL JUST BECAUSE IT'S NEW.

  • MODEL JUST BECAUSE IT'S NEW. SO THE MODEL VALIDATOR

  • SO THE MODEL VALIDATOR

  • SO THE MODEL VALIDATOR COMPONENT USES BEAM TO DO THAT

  • COMPONENT USES BEAM TO DO THAT

  • COMPONENT USES BEAM TO DO THAT COMPARISON USING CRITERIA THAT

  • COMPARISON USING CRITERIA THAT

  • COMPARISON USING CRITERIA THAT WE DEFINE TO DECIDE WHETHER OR

  • WE DEFINE TO DECIDE WHETHER OR

  • WE DEFINE TO DECIDE WHETHER OR NOT TO PUSH THE NEW MODEL TO

  • NOT TO PUSH THE NEW MODEL TO

  • NOT TO PUSH THE NEW MODEL TO PRODUCTION.

  • PRODUCTION.

  • PRODUCTION. IF MODEL VALIDATOR DECIDES THAT

  • IF MODEL VALIDATOR DECIDES THAT

  • IF MODEL VALIDATOR DECIDES THAT OUR NEW MODEL IS READY FOR

  • OUR NEW MODEL IS READY FOR

  • OUR NEW MODEL IS READY FOR PRODUCTION, THEN PUSHER DOES

  • PRODUCTION, THEN PUSHER DOES

  • PRODUCTION, THEN PUSHER DOES THE WORK OF ACTUALLY PUSHING IT

  • THE WORK OF ACTUALLY PUSHING IT

  • THE WORK OF ACTUALLY PUSHING IT TO OUR DEPLOYMENT TARGETS.

  • TO OUR DEPLOYMENT TARGETS.

  • TO OUR DEPLOYMENT TARGETS. THOSE TARGETS COULD BE

  • THOSE TARGETS COULD BE