Reproducible Machine Learning
As the field of machine learning continues to expand and break new ground, developing reliable and reproducible results is more essential than ever before.
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In this seminar, we’ll explore utilizing Weights & Biases, an experiment and data tracking platform, to centralize all of the information related to a given ML project while making it easy to share and communicate findings with colleagues. No matter where you’re storing your data or executing computation, Weights and Biases (W&B) will let you quickly track the entire process, from raw data through your final model. Let’s make our hard work organized and reproducible!
In this interactive session, we will reveal the "Magic Trio" - Iterate, Reproduce, and Collaborate - and demonstrate how they are the key to unlocking the true potential of reproducible machine learning. You'll discover how W&B can help you overcome the challenges associated with iterative experimentation, confidently reproduce results, and communicate effectively with your fellow data science wizards.
Teacher / speaker
Thomas Capelle is a Machine Learning engineer at Weights and Biases who works on the Growth Team. He is responsible for keeping the wandb/examples repository live and up to date. He also builds content on ML-OPS, application of wandb to industry and fun deep learning in general. Previously he was using deep learning to solve short-term forecasting for solar energy at Steady Sun. So, he has a background in Urban Planning, Combinatorial Optimization, Transportation Economics and Applied Mathematics.
VAIA and Trail Joint Seminar Series for researchers
In this seminar series, we bring together researchers that are interested in, or conducting research on AI and Machine Learning. Each VAIA-TRAIL doctoral course focuses on a specific topic ranging from times series to reinforcement learning to combat epidemics and interpretable & explainable Deep Learning.
