Key Ideas for Optimizing ML Training Pipeline and Model Accuracy
When it comes to allowing optimal performance for Machine Learning pipeline and models, taking a data-centric approach is key to success.
Why is Training Data the most powerful driver you need to leverage? How to leverage Training Data into an integrated end-to-end ML platform? How to accelerate model training through efficient ML workflows? Edouard d'Archimbaud, CTO & cofounder at Kili Technology and a former Head of Data & AI Lab at BNP Paribas, shares key learnings from the GAFAM (Facebook, Google).
*How Google targets the data to be annotated knowing that 50% of annotated data is redundant due to bad data sampling
*How Waymo (formerly the Google self-driving car project) uses Machine Learning to speed up annotation and annotation verification
*How Uber makes annotation fit into an end-to-end machine learning platform
Édouard d´Archimbaud is a Data Scientist and a CTO of Kili Technology. He co-founded the company in 2018 after holding various positions in research and operational projects at several banking institutions and investment funds. He led the Data Science and Artificial Intelligence Lab at BNP Paribas CIB. He graduated from the École Polytechnique with a specialization in Applied Mathematics and Computer Science, and obtained a Master's degree in Machine Learning from the École Normale Supérieure de Cachan.