Auto-complete Automation Deep Learning Metadata 
AWS Machine Learning Blog
APRIL 19, 2024
The platform both enables our AI—by supplying data to refine our models—and is enabled by it, capitalizing on opportunities for automated decision-making and data processing. Our deep learning models have non-trivial requirements: they are gigabytes in size, are numerous and heterogeneous, and require GPUs for fast inference and fine-tuning.
AWS Machine Learning Blog
SEPTEMBER 21, 2023
In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab , build an active learning framework on AWS to automate the processing of passenger documents. “In As part of this strategy, they developed an in-house passport analysis model to verify passenger IDs.
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AWS Machine Learning Blog
FEBRUARY 20, 2024
This time-consuming process must be completed before content can be dubbed into another language. Through automation, ZOO Digital aims to achieve localization in under 30 minutes. However, the supply of skilled people is being outstripped by the increasing demand for content, requiring automation to assist with localization workflows.
The MLOps Blog
JUNE 27, 2023
This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.
AWS Machine Learning Blog
MARCH 1, 2023
This requires not only well-designed features and ML architecture, but also data preparation and ML pipelines that can automate the retraining process. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately.
AWS Machine Learning Blog
FEBRUARY 19, 2024
As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity.
AWS Machine Learning Blog
JUNE 13, 2023
The integration of large language models helps humanize the interaction with automated agents, creating a more engaging and satisfying support experience. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. The following diagram illustrates our legacy architecture.
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