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Fueling the Future of GenAI with NiFi: Cloudera DataFlow 2.9 Delivers Enhanced Efficiency and Adaptability

Cloudera

For more than a decade, Cloudera has been an ardent supporter and committee member of Apache NiFi, long recognizing its power and versatility for data ingestion, transformation, and delivery. Accelerating GenAI with Powerful New Capabilities Cloudera DataFlow 2.9

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TensorFlow Transform: Ensuring Seamless Data Preparation in Production

Towards Data Science

ML Pipeline operations begins with data ingestion and validation, followed by transformation. The transformed data is trained and deployed. Initializing the InteractiveContext # This will create an sqlite db for storing the metadata context = InteractiveContext(pipeline_root=_pipeline_root) Next, we start with data ingestion.

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Looking Ahead: The Future of Data Preparation for Generative AI

Data Science Blog: Data Engineering

Businesses need to understand the trends in data preparation to adapt and succeed. If you input poor-quality data into an AI system, the results will be poor. This principle highlights the need for careful data preparation, ensuring that the input data is accurate, consistent, and relevant.

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Accelerate Your Data Mesh in the Cloud with Cloudera Data Engineering and Modak NabuTM

Cloudera

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

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Cloudera Data Platform extends Hybrid Cloud vision support by supporting Google Cloud

Cloudera

One of our customers, Commerzbank, has used the CDP Public Cloud trial to prove that they can combine both Google Cloud and CDP to accelerate their migration to Google Cloud without compromising data security or governance. . Data Preparation (Apache Spark and Apache Hive) .

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Bringing Automation To Data Labeling For Machine Learning With Watchful

Data Engineering Podcast

In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine learning data preparation and how it allows data engineers to be involved in the process. In fact, while only 3.5% That’s where our friends at Ascend.io In fact, while only 3.5%

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Enhancing Content Review: Proactively addressing threats with AutoML

LinkedIn Engineering

It enables models to stay updated by automatically retraining on incrementally larger and more recent data with a pre-defined periodicity. We also designed AutoML to support the addition of new algorithms to different components such as data-preprocessing, hyperparameter tuning, and metric computation.