AWS Machine Learning Blog

MARCH 15, 2024

Additionally, locally trained information can expose private data if reconstructed through an inference attack. To mitigate these risks, the FL model uses personalized training algorithms and effective masking and parameterization before sharing information with the training coordinator.

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Flipboard

JUNE 26, 2023

We use Amazon Neptune to visualize the customer data before and after the merge and harmonization. Overview of solution In this post, we go through the various steps to apply ML-based fuzzy matching to harmonize customer data across two different datasets for auto and property insurance.

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AWS Machine Learning Blog

MAY 3, 2023

Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it. The final outcome is an auto scaling, robust, and dynamically monitored solution.

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Dlabs.ai

JUNE 11, 2019

How to use deep learning (even if you lack the data)? To train a computer algorithm when you don’t have any data. Some would say, it’s impossible – but at a time where data is so sensitive, it’s a common hurdle for a business to face. Read on to learn how to use deep learning in the absence of real data.

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AWS Machine Learning Blog

JUNE 3, 2024

Complete the following steps: Choose Run Data quality and insights report. For Problem type , select Classification. For Data size , choose Sampled dataset. In the following example, we drop the columns Timestamp, Country, state, and comments, because these features will have least impact for classification of our model.

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ODSC - Open Data Science

APRIL 17, 2023

This frees up the data scientists to work on other aspects of their projects that might require a bit more attention. Without a deep understanding of underlying algorithms and techniques, novices can dip their toes in the waters of machine learning because PyCaret takes care of much of the heavy lifting for them.

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Data Science Auto-classification Machine Learning Data Scientist 59

The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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