Remove Auto-classification Remove Auto-complete Remove Big Data
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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

Flipboard

In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. The following diagram shows our solution architecture.

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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

Additionally, healthcare datasets often contain complex and heterogeneous data types, making data standardization and interoperability a challenge in FL settings. Because this data is across organizations, we use federated learning to collate the findings. Choose the Training Status tab and wait for the training run to complete.

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning Blog

Each business problem is different, each dataset is different, data volumes vary wildly from client to client, and data quality and often cardinality of a certain column (in the case of structured data) might play a significant role in the complexity of the feature engineering process.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

SageMaker Data Wrangler has also been integrated into SageMaker Canvas, reducing the time it takes to import, prepare, transform, featurize, and analyze data. In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. Can you see the complete model lineage with data/models/experiments used downstream? A self-service infrastructure portal for infrastructure and governance.

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A Straightforward Tutorial of Streamlit

Viso.ai

Machine learning extracts hidden information and insights from big data using statistical methods and techniques. After performing the data mining process, the next step is data visualization. It will assist the users and executives in identifying important information that is extracted from data.

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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data. Data: It is when specific data is selected arbitrarily and the generally agreed criteria are not followed. What are auto-encoders?