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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.

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Top Data Engineering Courses in 2024

Marktechpost

Data engineering is crucial in today’s digital landscape as organizations increasingly rely on data-driven insights for decision-making. Learning data engineering ensures proficiency in designing robust data pipelines, optimizing data storage, and ensuring data quality.

ETL 110
professionals

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State of Machine Learning Survey Results Part Two

ODSC - Open Data Science

Some of the issues make perfect sense as they relate to data quality, with common issues being bad/unclean data and data bias. What are the biggest challenges in machine learning? select all that apply) Related to the previous question, these are a few issues faced in machine learning.

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Use Amazon DocumentDB to build no-code machine learning solutions in Amazon SageMaker Canvas

AWS Machine Learning Blog

Add a new Amazon DocumentDB connection by choosing Import data , then choose Tabular for Dataset type. On the Import data page, for Data Source , choose DocumentDB and Add Connection. Enter a connection name such as demo and choose your desired Amazon DocumentDB cluster. Enter a user name, password, and database name.

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Enterprise LLM Summit highlights the importance of data development

Snorkel AI

Instead of exclusively relying on a singular data development technique, leverage a variety of techniques such as promoting, RAG, and fine-tuning for the most optimal outcome. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

LLM 69
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Snorkel AI partners with Snowflake to bring data-centric AI to the Snowflake Data Cloud

Snorkel AI

Data science and machine learning teams use Snorkel Flow’s programmatic labeling to intelligently capture knowledge from various sources such as previously labeled data (even when imperfect), heuristics from subject matter experts, business logic, and even the latest foundation models, then scale this knowledge to label large quantities of data.

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Snorkel AI partners with Snowflake to bring data-centric AI to the Snowflake Data Cloud

Snorkel AI

Data science and machine learning teams use Snorkel Flow’s programmatic labeling to intelligently capture knowledge from various sources such as previously labeled data (even when imperfect), heuristics from subject matter experts, business logic, and even the latest foundation models, then scale this knowledge to label large quantities of data.