I believe an approach to machine learning deployment that’s based on an industry standard, language-agnostic, and able to represent a broad range of algorithms is the clear path forward.

Accelerate the process of machine learning model development, evaluation, and deployment Help improve overall performance, accuracy, and efficiency of machine learning models ...

Alteryx Promote delivers AI/machine learning model deployment, management and integration Alteryx adds data science operationalizing functionality, based on technology from Brooklyn-based Yhat ...

From data collection, cleaning, and analysis - the amount of work required to prepare data for an machine learning model is very extensive ...

ParallelM, a provider of machine learning operationalization (MLOps) software, has released a new version of MCenter that includes REST-based serving using Kubernetes to create a no-code, autoscaling ...

For the highest chances of success in machine learning, test your model early with an MVP and invest the necessary time and money to diagnose and fix its weaknesses.

Designed to support the entire machine learning lifecycle -- from data ingestion and model training to deployment and monitoring -- Azure ML is empowering developers to integrate predictive ...