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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. You get the picture.

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The DataOps Vendor Landscape, 2021

DataKitchen

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 304
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The state of data quality in 2020

O'Reilly on Data

Just 20% of organizations publish data provenance and data lineage. Adopting AI can help data quality. Almost half (48%) of respondents say they use data analysis, machine learning, or AI tools to address data quality issues. Can AI be a catalyst for improved data quality?

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Two big things: They bring the messiness of the real world into your system through unstructured data. People have been building data products and machine learning products for the past couple of decades. Traditional versus GenAI software: Excitement builds steadilyor crashes after the demo. This isnt anything new.

Testing 174
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8 Modeling Tools to Build Complex Algorithms

Domino Data Lab

Before selecting a tool, you should first know your end goal – machine learning or deep learning. Machine learning identifies patterns in data using algorithms that are primarily based on traditional methods of statistical learning. It’s most helpful in analyzing structured data.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Inflexible schema, poor for unstructured or real-time data. Data lake Raw storage for all types of structured and unstructured data. Low cost, flexibility, captures diverse data sources. Easy to lose control, risk of becoming a data swamp. Exploratory analytics, raw and diverse data types.

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Build a decentralized semantic search engine on heterogeneous data stores using autonomous agents

AWS Big Data

Large language models (LLMs) such as Anthropic Claude and Amazon Titan have the potential to drive automation across various business processes by processing both structured and unstructured data. Redshift Serverless is a fully functional data warehouse holding data tables maintained in real time.