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.
The new dimension of AI: Vector Databases (Ep. 236)
Building Self Serve Business Intelligence With AI and LLMs at Zenlytic (Ep. 235)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner (Ep. 234)
Debunking AGI Hype and Embracing Reality (Ep. 233)
Full steam ahead! Unraveling Forward-Forward Neural Networks (Ep. 232)
The LLM Battle Begins: Google Bard vs ChatGPT (Ep. 231)
Unleashing the Force: Blending Neural Networks and Physics for Epic Predictions (Ep. 230)
AI’s Impact on Software Engineering: Killing Old Principles? [RB] (Ep. 229)
Warning! Mathematical Mayhem Ahead: Demystifying Liquid Time-Constant Networks (Ep. 228)
Efficiently Retraining Language Models: How to Level Up Without Breaking the Bank (Ep. 227)
Revolutionize Your AI Game: How Running Large Language Models Locally Gives You an Unfair Advantage Over Big Tech Giants (Ep. 226)
Rust: A Journey to High-Performance and Confidence in Code at Amethix Technologies (Ep. 225)
The Power of Graph Neural Networks: Understanding the Future of AI - Part 2/2 (Ep.224)
The Power of Graph Neural Networks: Understanding the Future of AI - Part 1/2 (Ep.223)
Leveling Up AI: Reinforcement Learning with Human Feedback (Ep. 222)
The promise and pitfalls of GPT-4 (Ep. 221)
AI’s Impact on Software Engineering: Killing Old Principles? (Ep. 220)
Edge AI applications for military and space [RB] (Ep. 219)
Prove It Without Revealing It: Exploring the Power of Zero-Knowledge Proofs in Data Science (Ep. 218)
Deep learning vs tabular models (Ep. 217)
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