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Unbundling the Graph in GraphRAG

O'Reilly Media

Store these chunks in a vector database, indexed by their embedding vectors. The various flavors of RAG borrow from recommender systems practices, such as the use of vector databases and embeddings. Here’s a simple rough sketch of RAG: Start with a collection of documents about a domain. Split each document into chunks.

Database 127
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9 Careers You Could Go into With a Data Science Degree

Smart Data Collective

The interdisciplinary field of data science involves using processes, algorithms, and systems to extract knowledge and insights from both structured and unstructured data and then applying the knowledge gained from that data across a wide range of applications. The average data scientist earns over $108,000 a year.

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Essential skills for blockchain development in 2025

Dataconomy

By spreading out data storage, blockchain reduces the vulnerability associated with centralized points of failure typical in traditional databases. In contrast to standard databases managed by a central authority, blockchain promotes direct peer-to-peer exchanges and renders middlemen unnecessary.

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10 industries that use distributed computing

IBM Journey to AI blog

The algorithms that empower AI and ML require large volumes of training data, in addition to strong and steady amounts of processing power. Database management is an area empowered by distributed computing, as are distributed databases, which perform faster by having tasks broken down into smaller actions.

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Data Intelligence empowers informed decisions

Pickl AI

Through advanced analytics and Machine Learning algorithms, they identify patterns such as popular products, peak shopping times, and customer preferences. Through statistical methods and advanced algorithms, we unravel patterns, trends, and valuable nuggets that guide decision-making. So, what is Data Intelligence with an example?

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Accelerate machine learning time to value with Amazon SageMaker JumpStart and PwC’s MLOps accelerator

AWS Machine Learning Blog

SageMaker covers the entire MLOps workflow, from collecting to preparing and training the data with built-in high-performance algorithms and sophisticated automated ML (AutoML) experiments so that companies can choose specific models that fit their business priorities and preferences.

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Models are part of chains and agents, supported by specialized tools like vector databases.