AWS Machine Learning Blog
JANUARY 5, 2024
The suite of services can be used to support the complete model lifecycle including monitoring and retraining ML models. Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step.
AWS Machine Learning Blog
NOVEMBER 9, 2023
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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The MLOps Blog
JUNE 27, 2023
This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Can you see the complete model lineage with data/models/experiments used downstream? The platform’s labeling capabilities include flexible label function creation, auto-labeling, active learning, and so on.
The MLOps Blog
MARCH 12, 2024
Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools. Observability tools: Use platforms that offer comprehensive observability into LLM performance, including functional logs (prompt-completion pairs) and operational metrics (system health, usage statistics).
Topbots
JUNE 29, 2023
On a more advanced stance, everyone who has done SQL query optimisation will know that many roads lead to the same result, and semantically equivalent queries might have completely different syntax. The manual collection of training data for Text2SQL is particularly tedious.
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