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Automating Model Risk Compliance: Model Monitoring

DataRobot Blog

A prerequisite in measuring a deployed model’s evolving performance is to collect both its input data and business outcomes in a deployed setting. With this data in hand, we are able to measure both the data drift and model performance, both of which are essential metrics in measuring the health of the deployed model.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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How to Build a CI/CD MLOps Pipeline [Case Study]

The MLOps Blog

Automation : Automating as many tasks to reduce human error and increase efficiency. Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. But we chose not to go with the same in our deployment due to a couple of reasons.

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Machine Learning Project Checklist

DataRobot Blog

Download the Machine Learning Project Checklist. Download Now. Machine learning and AI empower organizations to analyze data, discover insights, and drive decision making from troves of data. Discuss with stakeholders how accuracy and data drift will be monitored. Download Now.

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Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

AWS Machine Learning Blog

For instance, a notebook that monitors for model data drift should have a pre-step that allows extract, transform, and load (ETL) and processing of new data and a post-step of model refresh and training in case a significant drift is noticed.

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Protecting Marine Life with AI

Allen AI

These features make automated computer vision trained via supervised learning against expertly annotated datasets an attractive choice for satellite object detection. Data-flow depiction of a real-time streaming computer vision service for vessel detection in satellite imagery. mechanical turk is not an option).

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The Ever-growing Importance of MLOps: The Transformative Effect of DataRobot

DataRobot Blog

In the first part of the “Ever-growing Importance of MLOps” blog, we covered influential trends in IT and infrastructure, and some key developments in ML Lifecycle Automation. DataRobot MLOps counters potential delays with a management system that automates key processes. Download Now. DataRobot’s Robust ML Offering.