Taking AI from prototype to production - MFML Part 3

[@scign]

Index of steps 6-12 0:00:45 Step 6: Train models 0:25:14 Step 7: Tuning and debugging 0:43:45 Step 8: Validation 0:56:34 Step 9: Testing 1:27:25 Step 10: Production 1:53:11 Step 11: Launch 1:56:58 Step 12: Monitoring Steps 0-5: Step 0: Find an application where ML is useful - kzbin.info/www/bejne/onqpfZh-ecaho6M Step 1: Set objective - kzbin.info/www/bejne/onqpfZh-ecaho6M Step 2: Data engineering - kzbin.info/www/bejne/onqpfZh-ecaho6M Step 3: Split data - kzbin.info/www/bejne/onqpfZh-ecaho6M Step 4: Explore data - kzbin.info/www/bejne/onqpfZh-ecaho6M Step 5: Get tools - kzbin.info/www/bejne/onqpfZh-ecaho6M

[@piusaeneas92]

MACHINE LEARNING QUEEN 👑🙌

[@AdrienCaudron]

Most complex ML concepts and principles made simple - spot on as always and simply genial. Thanks Cassie!

[@scign]

My main takeaway is the debugging dataset - hardly any mention in the online material on this. Makes total sense that using the validation set for tuning will result in data leakage, overfitting and poorer generalization.

[@MLOps]

So brilliant! sharing this in the MLOps community now! thank you for putting that time into this!

[@TheArpan52]

Finally.... Loving this series❤️

[@etylio2]

I love this. You are amazing Cassie. Did you publish the 12 steps classical statistics course that you talked about ? Also, are you considering making a book out of this series ?

[@simplemente_humano]

1:12:24:“The p-value is the probability of obtaining a sample at least as extreme as the one we just observed in a world where the null hypothesis is actually true.” 1:13:10: “A small p-value makes your H₀ [null hypothesis] look ridiculous.” 1:17:10: "A p-value is the probability of getting a test performance at least as good as ours if the model is actually garbage."

[@Luisdi28ms]

Finally!!!! Will watch ASAP

[@subhayanroy2218]

Was waiting for this for so long

[@watercolorsongbird5597]

Make sure you have 10 times as many instances as features

[@AiEdgar]

She is impressive she has no ums, ah, or repetitions on over 6hrs of talk? she is stunning too. Damm

[@cicero8600]

Thank you for sharing and Merry Christmas!

[@parrotraiser6541]

There's some AI/ML-specific stuff in the middle there, but an awful lot of that sounds much the same as any other well-planned system launch.

[@oscarfernandez5119]

Awesome work! Are further videos going to be published?

[@vinhkhang20]

very informative

[@adiladawi]

more please

[@ajit60w]

Wonder Where she got the graphics in the slides

[@nonefvnfvnjnjnjevjenjvonej3384]

Beautiful person

[@AdamLindell]

This just proves my bigger C3P0 theory

[@Abdullahkbc]

speaking two hours even without drinking sth. hats off

[@kozyrkov]

It's nice to be seen and appreciated. Most people don't notice that.

[@GireesanNamboothiriP]

@@kozyrkov It may not be healthy. I used to teach and not having water had health issues. Amazing energy and very expressive way of telling the facts. I could relate to the mistakes I was doing most often :)

[@hussienalsafi1149]

☺️☺️☺️☺️☺️☺️☺️😘😘😘😘😘

[@ajit60w]

I beg to disagree that pvalue is hard to describe The coin tossing expt she conducted was a teachable moment. The audience member who picked up the coin said there was no tail marking!! That is surprising and low pvalue

[@3dascension744]

"But today the reality is that you should be doing this"