AWS Machine Learning

SEPTEMBER 6, 2023

Dataset overview The dataset used for this solution is pile-of-law within the Hugging Face repository. For this example, we use train.cc_casebooks.jsonl.xz For more information, refer to Amazon SageMaker Identity-Based Policy Examples. This dataset is a large corpus of legal and administrative data. within this repository.

Enterprise 95

Enterprise APIs Real estate Construction 95

AWS Machine Learning

DECEMBER 19, 2023

Now that you’ve gone through the creation and initial deployment, the MLOps engineer can configure failure alerts to be alerted for issues, for example, when a pipeline fails to do its intended job. About the Authors Kiran Kumar Ballari is a Principal Solutions Architect at Amazon Web Services (AWS).

Analytics 104

Analytics Engineering Government Best practices 104

AWS Machine Learning

DECEMBER 1, 2022

This post also provides an example end-to-end notebook and GitHub repository that demonstrates SageMaker geospatial capabilities, including ML-based farm field segmentation and pre-trained geospatial models for agriculture. Agronomic data platforms provide several layers of data and insights at scale.

APIs 74

APIs Analytics Engineering Technology 74

AWS Machine Learning

JANUARY 5, 2024

The drift notification emails will look similar to the examples in Figure 8. About the Authors Stephen Randolph is a Senior Partner Solutions Architect at Amazon Web Services (AWS). Drift data is enriched further with the addition of attributes for reporting purposes.

Management 110

Management APIs Engineering Government 110

Hodusoft

OCTOBER 26, 2023

In later years, STIR/SHAKEN was developed jointly by the SIP Forum and the Alliance for Telecommunications Industry Solutions (ATIS) to efficiently implement the Internet Engineering Task Force (IETF). In 1984, the idea got its first public trial with Bell Atlantic and a follow-up in 1987.

Telecommunications 52

Telecommunications Government Education voip 52

AWS Machine Learning

NOVEMBER 1, 2022

To demonstrate the orchestrated workflow, we use an example dataset regarding diabetic patient readmission. You can try out the approach with this example and experiment with additional data transformations following similar steps with your own datasets. For more information, refer to Amazon SageMaker Identity-Based Policy Examples.

Engineering 92

Engineering APIs Accountability industry solution 92

AWS Machine Learning

APRIL 25, 2023

We include an example of how to use the decorator function and the associated settings later in this post. In the following example code, we run a simple divide function as a SageMaker Training job: import boto3 import sagemaker from sagemaker.remote_function import remote sm_session = sagemaker.Session(boto_session=boto3.session.Session(region_name="us-west-2"))

Engineering 79

Engineering Metrics Construction Management 79