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.
Episode 40: Deep learning and image compression
Episode 39: What is L1-norm and L2-norm?
Episode 38: Collective intelligence (Part 2)
Episode 38: Collective intelligence (Part 1)
Episode 37: Predicting the weather with deep learning
Episode 36: The dangers of machine learning and medicine
Episode 35: Attacking deep learning models
Episode 34: Get ready for AI winter
Episode 33: Decentralized Machine Learning and the proof-of-train
Episode 32: I am back. I have been building fitchain
Founder Interview – Francesco Gadaleta of Fitchain
Episode 31: The End of Privacy
Episode 30: Neural networks and genetic evolution: an unfeasible approach
Episode 29: Fail your AI company in 9 steps
Episode 28: Towards Artificial General Intelligence: preliminary talk
Episode 27: Techstars accelerator and the culture of fireflies
Episode 26: Deep Learning and Alzheimer
Episode 25: How to become data scientist [RB]
Episode 24: How to handle imbalanced datasets
Episode 23: Why do ensemble methods work?
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