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.
Embedded Machine Learning: Part 5 - Machine Learning Compiler Optimization (Ep. 186)
Embedded Machine Learning: Part 4 - Machine Learning Compilers (Ep. 185)
Embedded Machine Learning: Part 3 - Network Quantization (Ep. 184)
Embedded Machine Learning: Part 2 (Ep. 183)
Embedded Machine Learning: Part 1 (Ep.182)
History of Data Science (Ep. 181)
Capturing Data at the Edge (Ep. 180)
[RB] Composable Artificial Intelligence (Ep. 179)
What is a data mesh and why it is relevant (Ep. 178)
Environmentally friendly AI (Ep. 177)
Do you fear of AI? Why? (Ep. 176)
Composable models and artificial general intelligence (Ep. 175)
Ethics and explainability in AI with Erika Agostinelli from IBM (ep. 174)
Is neural hash by Apple violating our privacy? (Ep. 173)
Fighting Climate Change as a Technologist (Ep. 172)
AI in the Enterprise with IBM Global AI Strategist Mara Pometti (Ep. 171)
Speaking about data with Mikkel Settnes from Dreamdata.io (Ep. 170)
Send compute to data with POSH data-aware shell (Ep. 169)
How are organisations doing with data and AI? (Ep. 168)
Don't fight! Cooperate. Generative Teaching Networks (Ep. 167)
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