coming back to this video after 2 years of working with tfx - this is a great resource for helping others to understand the 'why' of both MLOPS and TFX.
Robert, I love your style! Listening to you takes me on a journey (I am preparing for the Google ML Engineer Certification exam and the content you've created on TFX is absolutely unique). WELL DONE
what he's talking about is having fair unbiased data. For example, if you're building a speech-to-text model and the dataset only has the voices of a few people, it'll be hard for it to generalize to other voices.
![Summer Walker - Heart Of A Woman [Official Lyric Video]](http://i.ytimg.com/vi/SFddctbZzw8/mqdefault.jpg)
coming back to this video after 2 years of working with tfx - this is a great resource for helping others to understand the 'why' of both MLOPS and TFX.
Robert, I love your style! Listening to you takes me on a journey (I am preparing for the Google ML Engineer Certification exam and the content you've created on TFX is absolutely unique). WELL DONE
Great video, nice explain tfx production 🙏
One small tip: please don't use a thin orange line on the bottom of the thumbnail of the video. It looks like the video has already been seen.
oh man this is what i want u guys solve my major problem im so glad today
Nice job.
A more extended video would be interesting :)
Stay tuned!
thanks for sharing
would i be vendorlocked if i use tensorflow extended?
This is needed
Why it is called Tensorflow Extended? Does it only work on tensorflow models?
yes
hmm i watched the 3 videos in the reverse order because they are in the reverse order in the playlist
Where can I get all the videos
thnaks :)
Can it be set up on GPU and GPU can be utilised?
I still don't understand, what is TFX?
The Phoenix has risin'...
Attention all planets of the Solar Federation..? We have assumed control...Jupiter has Kronos in Check Mate
Time traveler: which DL framework do you prefer.
Everyone: pytorch
Time traveller: its 2018
198 Ephraim Mission
"unfairly" biased!? I'm pretty sure it's a WRONG bias and/or weights. Computers.. fairnes..? What?
what he's talking about is having fair unbiased data. For example, if you're building a speech-to-text model and the dataset only has the voices of a few people, it'll be hard for it to generalize to other voices.
This might help explain what we mean by fairness - ruclips.net/video/6CwzDoE8J4M/видео.html