AWS Machine Learning Blog

MAY 3, 2023

For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process.

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Auto-classification Auto-complete Machine Learning Metadata 70

Snorkel AI

FEBRUARY 2, 2023

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.

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Auto-classification Software Engineer Neural Network Machine Learning 52

Snorkel AI

FEBRUARY 2, 2023

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.

Auto-classification 52

Auto-classification Software Engineer Neural Network Machine Learning 52

DataRobot Blog

MARCH 10, 2022

With, now, native Python support delivered through Snowpark for Python, developers can leverage the vibrant collection of open-source data science and machine learning packages that have become household names, even at leading AI/ML enterprises. Consuming AI/ML Insights for Faster Decision Making.

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AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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The MLOps Blog

APRIL 20, 2023

Regularization techniques: experiment with weight decay, dropout, and data augmentation to improve model generalization. Managing data quality and quantity : managing data quality and quantity is crucial for training reliable CV models. Libraries like imgaug , albumentations , and torchvision.

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