Artificial Intelligence
Author: Shelbee Eigenbrode
Shelbee Eigenbrode is a Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She’s been in technology for 23 years, spanning multiple industries and technologies as well as multiple roles. She is currently focused on combining her background in DevOps and machine learning with the delivery and management of machine learning workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee is based in Denver, CO. In her spare time, she likes to spend time with her family, friends, and overactive dog.
Create SageMaker Pipelines for training, consuming and monitoring your batch use cases
Batch inference is a common pattern where prediction requests are batched together on input, a job runs to process those requests against a trained model, and the output includes batch prediction responses that can then be consumed by other applications or business functions. Running batch use cases in production environments requires a repeatable process for […]
Create Amazon SageMaker projects using third-party source control and Jenkins
Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can integrate Pipelines with existing CI/CD tooling. This includes integration with existing source control systems such as […]
Extend Amazon SageMaker Pipelines to include custom steps using callback steps
Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can extend your pipelines to include steps for tasks performed outside of Amazon SageMaker by taking advantage […]
Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio JupyterLab notebooks
April 2025: This post was reviewed and updated for accuracy. The Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio JupyterLab notebooks via CLI. The CLI eliminates the need to manually set up and connect to Docker build environments for building container images […]
Amazon Transcribe now supports speech-to-text in 31 languages
We recently announced that Amazon Transcribe now supports transcription for audio and video for 7 additional languages including Gulf Arabic, Swiss German, Hebrew, Japanese, Malay, Telugu, and Turkish languages. Using Amazon Transcribe, customers can now take advantage of 31 supported languages for transcription use cases such as improving customer service, captioning and subtitling, meeting accessibility requirements, and cataloging audio […]