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A Practical Guide to Multimodal Data Analytics

KDnuggets

BigQuery's ObjectRef unifies structured and unstructured data, enabling multimodal analytics via SQL and Python.

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Small Language Models, Big Impact: Fine-Tuning DistilGPT-2 for Medical Queries

Analytics Vidhya

Language models have transformed how we interact with data, enabling applications like chatbots, sentiment analysis, and even automated content generation. However, most discussions revolve around large-scale models like GPT-3 or GPT-4, which require significant computational resources and vast datasets.

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Introducing generative AI upgrades for Apache Spark in AWS Glue (preview)

AWS Big Data

To achieve this, we recommend specifying a run configuration when starting an upgrade analysis as follows: Using non-production developer accounts and selecting sample mock datasets that represent your production data but are smaller in size for validation with Spark Upgrades. 2X workers and auto scaling enabled for validation.

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What is data architecture? A framework to manage data

CIO Business Intelligence

Data architectures should integrate with legacy applications using standard API interfaces. They should also be optimized to share data across systems, geographies, and organizations. Real-time data enablement. Be decoupled and extensible.

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How Skroutz handles real-time schema evolution in Amazon Redshift with Debezium

AWS Big Data

When we decided to build our own data platform to meet our data needs, such as supporting reporting, business intelligence (BI), and decision-making, the main challenge—and also a strict requirement—was to make sure it wouldn’t block or delay our product development.

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Unlocking AI Potential: Community-Driven Innovations with InstructLab

Decision Management Solutions

By utilizing InstructLab’s synthetic data generation and fine-tuning methodologies, we can efficiently train our models to understand and interpret complex code structures and decision logic.

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Top Predictive Analytics Models and Algorithms to Know

Jet Global

Machine learning algorithms used for prediction analyze historical data to forecast future outcomes. These algorithms, including linear regression, decision trees, and neural networks, identify patterns and relationships within the data, enabling accurate predictions and informed decision-making.