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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization. Alerts are raised whenever anomalies are detected.

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The Sequence Pulse: The Architecture Powering Data Drift Detection at Uber

TheSequence

Not surprisingly, data quality and drifting is incredibly important. Many data drift error translates into poor performance of ML models which are not detected until the models have ran. A recent study of data drift issues at Uber reveled a highly diverse perspective.

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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production. The typical score.py

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Building ML Platform in Retail and eCommerce

The MLOps Blog

How to set up an ML Platform in eCommerce? The objective of an ML Platform is to automate repetitive tasks and streamline the processes starting from data preparation to model deployment and monitoring. An ML Platform helps in the faster iteration of an ML project lifecycle. via Data Connectors.

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

The MLOps Blog

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools.