Accountability Big data Government Metrics 
AWS Machine Learning
DECEMBER 1, 2022
Overview of model governance. Model governance is a framework that gives systematic visibility into model development, validation, and usage. Model governance is applicable across the end-to-end ML workflow, starting from identifying the ML use case to ongoing monitoring of a deployed model through alerts, reports, and dashboards.
AWS Machine Learning
JULY 19, 2023
They provide a factsheet of the model that is important for model governance. However, when solving a business problem through a machine learning (ML) model, as customers iterate on the problem, they create multiple versions of the model and they need to operationalize and govern multiple model versions.
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Totango
DECEMBER 1, 2021
An agile approach brings the full power of big data analytics to bear on customer success. Follow a clear plan on governance and decision making. This provides transparency and accountability and empowers a data-driven approach to customer success. Follow a Clear Plan on Governance and Decision making.
AWS Machine Learning
JANUARY 26, 2024
Consider your security posture, governance, and operational excellence when assessing overall readiness to develop generative AI with LLMs and your organizational resiliency to any potential impacts. You should begin by extending your existing security, assurance, compliance, and development programs to account for generative AI.
AWS Machine Learning
FEBRUARY 29, 2024
Model governance – The Amazon SageMaker Model Registry integration allows for tracking model versions, and therefore promoting them to production with confidence. Each modeling unit is a sequence of up to six steps for training an ML model: process, train, create model, transform, metrics, and register model. The model_unit.py
AWS Machine Learning
NOVEMBER 24, 2023
In contrast, the data science and analytics teams already using AWS directly for experimentation needed to also take care of building and operating their AWS infrastructure while ensuring compliance with BMW Group’s internal policies, local laws, and regulations. A data scientist team orders a new JuMa workspace in BMW’s Catalog.
AWS Machine Learning
MAY 8, 2023
It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. This can be a challenge for enterprises in regulated industries that need to keep strong model governance for audit purposes.
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