Remove Auto-classification Remove Categorization Remove Data Quality
article thumbnail

Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

article thumbnail

How Pixability uses foundation models to accelerate NLP application development by months

Snorkel AI

Pixability is a data and technology company that allows advertisers to quickly pinpoint the right content and audience on YouTube. To help brands maximize their reach, they need to constantly and accurately categorize billions of YouTube videos. Using AI to help customers optimize ad spending and maximize their reach on YouTube.

NLP 52
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

How Memorial Sloan Kettering Cancer Center (MSKCC) used Snorkel Flow to scale clinical trial screening

Snorkel AI

Scaling clinical trial screening with document classification Memorial Sloan Kettering Cancer Center, the world’s oldest and largest private cancer center, provides care to increase the quality of life of more than 150,000 cancer patients annually. Watch this and many other sessions on-demand at future.snorkel.ai.

article thumbnail

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab.

article thumbnail

Building better datasets with Snorkel Flow error analysis

Snorkel AI

If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs.

article thumbnail

Building better datasets with Snorkel Flow error analysis

Snorkel AI

If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs.

article thumbnail

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

It includes processes for monitoring model performance, managing risks, ensuring data quality, and maintaining transparency and accountability throughout the model’s lifecycle. It’s a binary classification problem where the goal is to predict whether a customer is a credit risk. region_name ram_client = boto3.client('ram')

ML 106