Deploy Your Predictive Model To Production - Machine Learning Mastery


Often the complexity a machine learning algorithms is in the model training, not in making predictions. I also strongly recommend gathering outlier and interesting cases from operations over time that produce unexpected results (or break the system). Like a ratchet, consider incrementally updating performance requirements as model performance improves. If you're interested in more information on operationalizing machine learning models check out the post: This is more on the Google-scale machine learning model deployment.

Amazon Joins Tech Giants in Open Sourcing a Key Machine Learning Tool


"DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale. DSSTNE is built for production deployment of real-world deep learning applications, emphasizing speed and scale over experimental flexibility. "Deep Scalable Sparse Tensor Network Engine, (DSSTNE), pronounced "Destiny", is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models.