Zero-overhead scalable machine learning

By Peter Zhokhov, Senior Data Scientist We analyze the complexity overhead and a learning curve associated with the transition from quick-and-dirty machine learning experiments to large-scale production-grade models with the recently released Amazon SageMaker and open-source project Studio.ML. Virtually every domain of human expertise is facing a rapid increase in the integration of machine learning…

Introducing Studio.ML: an Open Source Framework that Simplifies and Expedites Machine Learning Model Development

By Arshak Navruzyan VP Distributed Artificial Intelligence Platform At Sentient, we are continuously building and iterating on machine learning models to advance our products and research. We’re big fans of Keras, TensorFlow, PyTorch etc., but along the way we’ve found some critical missing features when it comes to running a large set of machine learning…