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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

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.

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Tensorflow Data Validation

Mlearning.ai

Auto Data Drift and Anomaly Detection Photo by Pixabay This article is written by Alparslan Mesri and Eren Kızılırmak. Model performance may change over time due to data drift and anomalies in upcoming data. This can be prevented using Google’s Tensorflow Data Validation library.

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How to Practice Data-Centric AI and Have AI Improve its Own Dataset

ODSC - Open Data Science

Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. Data issues like label errors, outliers, duplicates, data drift, and low-quality examples significantly hamper model performance.

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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 Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Amazon SageMaker Ground Truth SageMaker Ground Truth is a fully managed data labeling service designed to help you efficiently label and annotate your training data with high-quality annotations. Can you render audio/video?

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

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).