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Automatic Feature Engineering: An Event-Driven Approach

Dataiku

TL;DR : As data science evolves as a discipline, we think that it’s time to have a more systematic approach to feature engineering. That’s why we built EventsAggregator , an automatic feature engineering framework that leverages user’s knowledge to generate expressive features.

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Event-driven architecture (EDA) enables a business to become more aware of everything that’s happening, as it’s happening 

IBM Big Data Hub

In modern enterprises, where operations leave a massive digital footprint, business events allow companies to become more adaptable and able to recognize and respond to opportunities or threats as they occur. This approach then enables them to build new responsive applications.

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AIOps reimagines hybrid multicloud platform operations

IBM Big Data Hub

But this traditional approach introduces more challenges. As shown in the following diagram, an issue in the environment triggers several events across the full stack of the business solution. This results in an unmanageable event flood. This is usually not enough time for a human to resolve an issue. What is the solution?

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The software-defined vehicle: The architecture behind the next evolution of the automotive industry

IBM Big Data Hub

They seek full integration into their digital lives, desiring a vehicle that can manage their operations, add functionality and enable new features primarily or entirely through software. More and more consumers now expect their vehicles to offer an experience no different from that offered by other smart devices. between 2023 and 2032.

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Set up alerts and orchestrate data quality rules with AWS Glue Data Quality

AWS Big Data

This proactive approach helps mitigate the risk of making decisions based on inaccurate information. We are excited to announce that AWS Glue Data Quality is now generally available, offering built-in integration with Amazon EventBridge and AWS Step Functions to streamline event-driven data quality management.

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Microsoft Azure OpenAI Service and DataRobot Modernize Data Science Work with Cutting-Edge Technology Innovations

DataRobot Blog

However, we’re now taking the information managed by DataRobot (such as the data , features , models , predictions ) and leveraging the capabilities of the Azure OpenAI Service to make it more accessible and understandable. An example of this is proposing why a feature has a high impact on predictions.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

For many enterprises and large organizations, it is not feasible to have one processing engine or tool to deal with the various business requirements. Apache Iceberg is an open-source table format that provides reliability, simplicity, and high performance for large datasets with transactional integrity between various processing engines.