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Building Massively Scalable Machine Learning Pipelines with Microsoft Synapse ML

KDnuggets

The new platform provides a single API to abstract dozens of ML frameworks and databases.

ML 369
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Traditional vs Vector databases: Your guide to make the right choice

Data Science Dojo

With the rapidly evolving technological world, businesses are constantly contemplating the debate of traditional vs vector databases. Hence, databases are important for strategic data handling and enhanced operational efficiency. Hence, databases are important for strategic data handling and enhanced operational efficiency.

Database 370
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Complete Guide to Effortless ML Monitoring with Evidently.ai

Analytics Vidhya

Introduction Whether you’re a fresher or an experienced professional in the Data industry, did you know that ML models can experience up to a 20% performance drop in their first year? ML Monitoring aids in early […] The post Complete Guide to Effortless ML Monitoring with Evidently.ai

ML 319
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BigQuery: An Walkthrough of ML with Conventional SQL

Analytics Vidhya

The post BigQuery: An Walkthrough of ML with Conventional SQL appeared first on Analytics Vidhya. Machine learning is an increasingly popular and developing trend among us. BigQueryML is a toolset that will allow us to build machine learning models by executing […].

SQL 348
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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning Blog

By setting up automated policy enforcement and checks, you can achieve cost optimization across your machine learning (ML) environment. The following table provides examples of a tagging dictionary used for tagging ML resources. A reference architecture for the ML platform with various AWS services is shown in the following diagram.

ML 109
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What Is a Lakebase?

databricks

Published: June 11, 2025 Announcements 5 min read by Ali Ghodsi , Stas Kelvich , Heikki Linnakangas , Nikita Shamgunov , Arsalan Tavakoli-Shiraji , Patrick Wendell , Reynold Xin and Matei Zaharia Share this post Keep up with us Subscribe Summary Operational databases were not designed for today’s AI-driven applications.

Database 204
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How Crexi achieved ML models deployment on AWS at scale and boosted efficiency

AWS Machine Learning Blog

With the current demand for AI and machine learning (AI/ML) solutions, the processes to train and deploy models and scale inference are crucial to business success. Even though AI/ML and especially generative AI progress is rapid, machine learning operations (MLOps) tooling is continuously evolving to keep pace.

AWS 118