embeddings
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Explore more → **embeddings**
What is embeddings?
Embeddings are numerical representations of data, such as words, images, or other complex objects. These representations capture semantic relationships and facilitate machine learning tasks like similarity search, classification, and recommendation. They are commonly used in natural language processing, computer vision, and various other fields to transform complex data into a format that machine learning models can effectively process.
What other technologies are related to embeddings?
embeddings Competitor Technologies
FAISS
FAISS is a library for efficient similarity search and clustering of dense vectors, competing with other vector database technologies.
Pinecone
Pinecone is a vector database service, competing with other vector database technologies like Weaviate and FAISS.
Weaviate
Weaviate is a vector database, competing with other vector database technologies like Pinecone and FAISS.
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embeddings Complementary Technologies
vectorization
Vectorization is a crucial step in preparing data for use with vector databases and RAG systems, converting data into numerical vectors.
vector databases
Vector databases are essential for storing and retrieving vector embeddings used in RAG and other LLM applications.
Prompt Engineering
Prompt engineering enhances the performance of LLMs, especially in RAG and fine-tuning scenarios, by crafting effective prompts.
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Which job functions commonly mention embeddings?
AI Engineer
0.4% of all AI Engineer jobs mention embeddings
View 350
jobs on Sumble
Machine Learning
0.1% of all Machine Learning jobs mention embeddings
View 143
jobs on Sumble
Data Scientist
0.1% of all Data Scientist jobs mention embeddings
View 311
jobs on Sumble
Applied Scientist
0.1% of all Applied Scientist jobs mention embeddings
View 12
jobs on Sumble
Data, Analytics & Machine Learning
813 Data, Analytics & Machine Learning jobs mention embeddings
View 813
jobs on Sumble
Which organizations are mentioning embeddings?
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