Archive for the 'pipeline' Category

Scaling technology and business processes are not equal. Since the beginning of the enterprise technology, scaling software has been a difficult task to get right inside large organisations. When it comes to Artificial Intelligence and Machine Learning, it becomes vastly more complicated. 

In this episode I propose a framework - in five pillars - for the business side of artificial intelligence.

 

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In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. 

I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github 

 

References

Videflow Github official repository
https://github.com/videoflow/videoflow

 

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Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense.

Enjoy the show!

 

References

Faster Neural Network Training with Data Echoing
https://arxiv.org/abs/1907.05550

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In this episode I speak about how important reproducible machine learning pipelines are.
When you are collaborating with diverse teams, several tasks will be distributed among different individuals. Everyone will have good reasons to change parts of your pipeline, leading to confusion and definitely a number of options that soon explode.
In all those cases, tracking data and code is extremely helpful to build models that are reproducible anytime, anywhere.
Listen to the podcast and learn how.

 

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