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Zero-overhead scalable machine learning-Part 2

By Peter Zhokhov, Senior Data Scientist A common obstacle in sharing results of machine learning research with industry data science is reproducibility of experiments. The research community often has their own dedicated machines and clusters; whereas industry often relies on cloud compute providers such as AWS EC2 or Google Compute Engine. As a result, even…

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Q&A with Sentient: on Artificial Intelligence, Open Source Machine Learning Model Management Framework, and the Studio.ML Platform

Reposted from SourceForge.net The meteoric rise of artificial intelligence (AI), particularly machine learning (ML), is significantly disrupting the business landscape and spurring workforce change across different industries such as manufacturing, transportation, retailing, finance, healthcare, advertising, insurance, entertainment, and more. As businesses enter into a new era ruled by data, organizations can now leverage machine learning…

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Zero-overhead scalable machine learning-Part 1

By Peter Zhokhov, Senior Data Scientist We analyze the complexity overhead and a learning curve associated with the transition from quick-and-dirty machine learning experiments to large-scale production-grade models with the recently released Amazon SageMaker and open-source project Studio.ML.   Virtually every domain of human expertise is facing a rapid increase in the integration of machine learning solutions and…

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