Accountability Examples industry solution 
AWS Machine Learning
SEPTEMBER 6, 2023
For this example, we use train.cc_casebooks.jsonl.xz Prerequisites Before getting started, make sure you have the following prerequisites: An AWS account. 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.
AWS Machine Learning
AUGUST 21, 2024
We also explore best practices for optimizing your batch inference workflows on Amazon Bedrock, helping you maximize the value of your data across different use cases and industries. Solution overview The batch inference feature in Amazon Bedrock provides a scalable solution for processing large volumes of data across various domains.
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AWS Machine Learning
SEPTEMBER 18, 2024
The following is an example of a synthetically generated offering for the construction industry: OneCompany Consulting Construction Consulting Services Offerings Introduction OneCompany Consulting is a premier construction consulting firm dedicated to. Our examples were manually created only for high-level guidance for simplicity.
AWS Machine Learning
DECEMBER 19, 2023
Customers can configure an AWS account, the repository, the model, the data used, the pipeline name, the training framework, the number of instances to use for training, the inference framework, and any pre- and post-processing steps and several other configurations to check the model quality, bias, and explainability.
AWS Machine Learning
NOVEMBER 1, 2022
According to a Forbes survey , there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. To demonstrate the orchestrated workflow, we use an example dataset regarding diabetic patient readmission. Prerequisites. An S3 bucket.
AWS Machine Learning
JANUARY 5, 2024
The drift notification emails will look similar to the examples in Figure 8. Across accounts, automate deployment using export and import dataset, data source, and analysis API calls provided by QuickSight. About the Authors Stephen Randolph is a Senior Partner Solutions Architect at Amazon Web Services (AWS).
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"))
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