Distributed Training on Amazon SageMaker delivers new capabilities that can train large models up to two times faster than would otherwise be possible with today's machine learning processors Inc. company, announced nine new capabilities for its industry-leading machine learning service, Amazon SageMaker, making it even easier for developers to automate and scale all steps of the end-to-end machine learning workflow. Today's announcements bring together powerful new capabilities like faster data preparation, a purpose-built repository for prepared data, workflow automation, greater transparency into training data to mitigate bias and explain predictions, distributed training capabilities to train large models up to two times faster, and model monitoring on edge devices. Machine learning is becoming more mainstream, but it is still evolving at a rapid clip. With all the attention machine learning has received, it seems like it should be simple to create machine learning models, but it isn't. In order to create a model, developers need to start with the highly manual process of preparing the data.

See the ModelDB frontend in action: ModelDB is an end-to-end system to manage machine learning models. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB can be used with any ML environment via the ModelDB Light API. ModelDB native clients can be used for advanced support in and . The ModelDB frontend provides rich summaries and graphs showing model data.

Slowly but inevitably machine learning is starting to influence our daily lives. Whether you ask your home virtual assistant to check the weather forecast or cede some of the planning (and even driving) of your daily commute to your "smart" automobile, it is machine learning that is making life easier. Yet while we seem to have embraced machine learning at home, understanding and embracing its potential in the enterprise remains challenging. Judging by the experience I've had with our clients, the desire is there, experiments are happening, but there is difficulty in getting real change into production. Many organizations are not yet making the transformational changes driven from machine learning that will be needed in order to succeed in the coming years.