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How to Practice Data-Centric AI and Have AI Improve its Own Dataset

ODSC - Open Data Science

Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. Data issues like label errors, outliers, duplicates, data drift, and low-quality examples significantly hamper model performance.

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Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

DataRobot Blog

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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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

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.