AWS Machine Learning

NOVEMBER 9, 2023

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 95

Scripts Accountability Engineering Best practices 95

AWS Machine Learning

FEBRUARY 29, 2024

You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. framework/createmodel/ – This directory contains a Python script that creates a SageMaker model object based on model artifacts from a SageMaker Pipelines training step. script is used by pipeline_service.py The model_unit.py

Scripts 87

Scripts Engineering Metrics Best practices 87

AWS Machine Learning

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

Best practices 117

Best practices Government Engineering Accountability 117

AWS Machine Learning

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account).

Scripts 71

Scripts APIs Enterprise Accountability 71

AWS Machine Learning

MARCH 23, 2023

To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker. default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket resource("s3").Bucket Bucket (bucket).Object

Scripts 91

Scripts APIs Government Analytics 91

AWS Machine Learning

MAY 8, 2023

How to use MLflow as a centralized repository in a multi-account setup. Prerequisites Before deploying the solution, make sure you have access to an AWS account with admin permissions. You can use this script add_users_and_groups.py How to extend Studio to enhance the user experience by rendering MLflow within Studio.

APIs 69

APIs Government Accountability Scripts 69

Callminer

SEPTEMBER 14, 2020

Business analysts working in call center operations must have a thorough understanding of regulatory requirements to ensure that processes, technology solutions and other strategic initiatives are compliant with all relevant regulations, such as PCI-DSS or the Fair Debt Collection Practices Act (FDCPA).

Call Center 182

Call Center Analytics First call resolution Metrics 182