Amazon Web Services (AWS) has added nine capabilities to its SageMaker machine-learning service, making it easier for developers to automate and scale all steps of the end-to-end machine-learning workflow.

This brings together capabilities such as 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 rapidly. To create a model, developers need to start with the manual process of preparing the data. Then they need to visualise it in notebooks, pick the right algorithm, set up the framework, train the model, tune millions of possible parameters, deploy the model, and monitor its performance. This process needs to be continuously repeated to ensure the model is performing as expected over time.

In the past, this process put machine learning out of the reach of all but the most skilled developers. However, SageMaker has changed that. SageMaker is a fully managed service that removes challenges from each stage of the machine-learning process, making it easier and faster for everyday developers and data scientists to build, train and deploy machine-learning models.

This announcement builds on the more than 50 SageMaker capabilities that AWS has delivered in the past year to make it easier for developers and data scientists to prepare, build, train, deploy and manage machine learning models, including:

• Data Wrangler automated data preparation: This provides a fast and easy way to prepare data for machine learning.
• Feature Store storage and management: This provides a repository that makes it easy to store, update, retrieve and share machine-learning features for training and inference.
• Pipelines workflow management and automation: This is a purpose-built, easy-to-use continuous integration and continuous delivery service for machine learning.
• Clarify bias detection and explainability: This provides bias detection across the machine-learning workflow, enabling developers to build fairness and transparency into their machine-learning models.
• Deep profiling for SageMaker debugger model training: This lets developers train their models faster by automatically monitoring system resource use and providing alerts for training bottlenecks.
• Distributed training on SageMaker: This makes it possible to train large, complex deep learning models up to two times faster than current approaches.
• Edge Manager model management for edge devices: This allows developers to optimise, secure, monitor and maintain machine-learning models deployed on fleets of edge devices.
• JumpStart: This provides developers an easy-to-use, searchable interface to find algorithms and sample notebooks.

“Hundreds of thousands of everyday developers and data scientists have used our industry-leading machine-learning service, Amazon SageMaker, to remove barriers to building, training and deploying custom machine-learning models,” said Swami Sivasubramanian, AWS vice president. “One of the best parts about having such a widely-adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables. Today, we are announcing a set of tools for SageMaker that makes it much easier for developers to build end-to-end machine-learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine-learning models with greater visibility, explainability and automation at scale.”