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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

• 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

• 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. 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