Remove Blog Remove Data Science Remove Metadata
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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Enterprise data is brought into data lakes and data warehouses to carry out analytical, reporting, and data science use cases using AWS analytical services like Amazon Athena , Amazon Redshift , Amazon EMR , and so on. Table metadata is fetched from AWS Glue. The generated Athena SQL query is run.

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Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale

KDnuggets

Its static snapshot and lack of detailed metadata limit modern applicability. While impressive in volume, it offers minimal metadata and prioritizes click-through rate (CTR) over recommendation logic. However, the data is notoriously sparse, with a steep drop-off in interaction for most users and products.

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Building a Custom PDF Parser with PyPDF and LangChain

KDnuggets

Install them with: pip install pypdf langchain If you want to manage dependencies neatly, create a requirements.txt file with: pypdf langchain requests And run: pip install -r requirements.txt Step 1: Set Up the PDF Parser(parser.py) The core class CustomPDFParser uses PyPDF to extract text and metadata from each PDF page.

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MLFlow Mastery: A Complete Guide to Experiment Tracking and Model Management

KDnuggets

mlruns This command uses an SQLite database for metadata storage and saves artifacts in the mlruns directory. This format includes the model and its metadata. Metadata has the models framework, version, and dependencies. Launching the MLFlow UI The MLFlow UI is a web-based tool for visualizing experiments and models.

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Generative AI: A Self-Study Roadmap

KDnuggets

Preprocessing steps like cleaning formatting, extracting metadata, and creating document summaries improve retrieval accuracy. For example, a marketing content generator that produces blog posts, social media content, and email campaigns based on product information and target audience.

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

SageMaker Lakehouse enables seamless data access directly in the new SageMaker Unified Studio and provides the flexibility to access and query your data with all Apache Iceberg-compatible tools on a single copy of analytics data. Having confidence in your data is key. The tools to transform your business are here.

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Data Quality Testing: A Shared Resource for Modern Data Teams

DataKitchen

They establish quality metrics, set thresholds, and collaborate with upstream systems to identify and address the root causes of data issues. Data Governance Teams: Data Governance professionals employ quality testing as a means to enhance data catalogs with high-quality metadata.