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

AWS Machine Learning Blog

However, there are many clear benefits of modernizing our ML platform and moving to Amazon SageMaker Studio and Amazon SageMaker Pipelines. Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution. Workflow B corresponds to model quality drift checks.

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Arize AI on How to apply and use machine learning observability

Snorkel AI

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. We have four pillars that we use when thinking about ML observability.

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Arize AI on How to apply and use machine learning observability

Snorkel AI

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. We have four pillars that we use when thinking about ML observability.

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The Age of Health Informatics: Part 1

Heartbeat

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms. We pay our contributors, and we don't sell ads.

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Importance of Machine Learning Model Retraining in Production

Heartbeat

Once the best model is identified, it is usually deployed in production to make accurate predictions on real-world data (similar to the one on which the model was trained initially). Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But this is only sometimes the case.

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The Future of Data-Centric AI Day 2: Snorkel Flow and Beyond

Snorkel AI

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance. He also described a near future where large companies will augment the performance of their finance and tax professionals with large language models, co-pilots, and AI agents.

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The Future of Data-Centric AI Day 2: Snorkel Flow and Beyond

Snorkel AI

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance. He also described a near future where large companies will augment the performance of their finance and tax professionals with large language models, co-pilots, and AI agents.