PostgresML vs Wander：怎么选？

关于 PostgresML

Hear from our communityThis is why I’m bullish on@postgresml- devs will always prefer to do things in data stores they already use in productionJames yu@jamesyuGreat article by PostgresML, running@huggingfacemodels INSIDE@PostgreSQLnice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"Paul Copplestone@kiwicoppleLove the fact that@postgresmlcan run various algorithms to find the optimum one for model creationRebataurAI@rebataurYou can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functionsDushyant (e/acc)@DevDminGodIf you want to seamlessly integrate machine learning models into your#PostgreSQLdatabase, use PostgresML.Khuyen Tran@KhuyenTran16? there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvectorMartin McFly@martinmarkTons of capability in that Postgres extension. It's an important part of the ML Stack atcloud.tembo.ioas well.Adam Hendel@adamhendelA game-changer indeed! By integrating ML and AI directly at the database level with@postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.Pranay Suyash@pranaysuyashThis is why I’m bullish on@postgresml- devs will always prefer to do things in data stores they already use in productionJames yu@jamesyuGreat article by PostgresML, running@huggingfacemodels INSIDE@PostgreSQLnice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"Paul Copplestone@kiwicoppleLove the fact that@postgresmlcan run various algorithms to find the optimum one for model creationRebataurAI@rebataurYou can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functionsDushyant (e/acc)@DevDminGodIf you want to seamlessly integrate machine learning models into your#PostgreSQLdatabase, use PostgresML.Khuyen Tran@KhuyenTran16? there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvectorMartin McFly@martinmarkTons of capability in that Postgres extension. It's an important part of the ML Stack atcloud.tembo.ioas well.Adam Hendel@adamhendelA game-changer indeed! By integrating ML and AI directly at the database level with@postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.Pranay Suyash@pranaysuyash

What makes PostgresMLso powerfulIndex, filter and re-rank vector embeddings10x faster vector operationsPerform fast KNN and ANN searchIndex embeddings with HNSW or IVFFlatLearn Morearrow_forwardGenerate embeddingsChoose from state-of-the-art modelsBuilt-in data preprocessors for splitting and chunkingConvert text to vector embeddingsLearn Morearrow_forwardColocate data and computeEmbed, serve and store all in one processTerabytes of data on a single machineBuilt-in data privacy & securityTrain, tune and deployRegression, classification and clusteringFine-tune LLMs on your own dataMonitor model deployments over timeLearn Morearrow_forwardGet the most of LLMsUse open-source models (Mistral, LLama, etc.)Perform a range of NLP tasksServe with the same infrastructureLearn Morearrow_forwardComprehensive platformMultiple deployment optionsPerform several AI & machine learning tasksUse SQL or SDKs in JS and PythonIndex, filter and re-rank vector embeddings10x faster vector operationsPerform fast KNN and ANN searchIndex embeddings with HNSW or IVFFlatLearn Morearrow_forwardGenerate embeddingsChoose from state-of-the-art modelsBuilt-in data preprocessors for splitting and chunkingConvert text to vector embeddingsLearn Morearrow_forwardColocate data and computeEmbed, serve and store all in one processTerabytes of data on a single machineBuilt-in data privacy & securityTrain, tune and deployRegression, classification and clusteringFine-tune LLMs on your own dataMonitor model deployments over timeLearn Morearrow_forwardGet the most of LLMsUse open-source models (Mistral, LLama, etc.)Perform a range of NLP tasksServe with the same infrastructureLearn Morearrow_forwardComprehensive platformMultiple deployment optionsPerform several AI & machine learning tasksUse SQL or SDKs in JS and Python

This is why I’m bullish on@postgresml- devs will always prefer to do things in data stores they already use in productionJames yu@jamesyuGreat article by PostgresML, running@huggingfacemodels INSIDE@PostgreSQLnice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"Paul Copplestone@kiwicoppleLove the fact that@postgresmlcan run various algorithms to find the optimum one for model creationRebataurAI@rebataurYou can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functionsDushyant (e/acc)@DevDminGodIf you want to seamlessly integrate machine learning models into your#PostgreSQLdatabase, use PostgresML.Khuyen Tran@KhuyenTran16? there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvectorMartin McFly@martinmarkTons of capability in that Postgres extension. It's an important part of the ML Stack atcloud.tembo.ioas well.Adam Hendel@adamhendelA game-changer indeed! By integrating ML and AI directly at the database level with@postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.Pranay Suyash@pranaysuyash

核心功能

• filter_dramaPostgresML Cloud（自动优化版）
• vpn_keyVPC（自动优化版）
• descriptionLLMs（自动优化版）
• subtitlesEmbeddings（自动优化版）
• open_withVector Database（自动优化版）
• model_trainingSupervised Learning（自动优化版）
• manage_searchRAG（自动优化版）
• feature_searchSearch（自动优化版）

Wander是一款致力于帮助旅行者找到志同道合伙伴的AI工具。它通过智能匹配算法，将用户的旅行偏好、兴趣和性格特征与其他人进行精准对接，从而让独自旅行或寻找合适旅伴变得简单高效。核心功能包括基于兴趣图谱的智能匹配，让用户能快速找到目的地相同、节奏合拍的伙伴；同时提供群组讨论和行程规划工具，方便结伴后协调细节；此外，AI还会根据用户反馈持续优化推荐，提升匹配质量。这款工具适合所有热爱旅行但不愿独自上路的人，无论是背包客、自由行爱好者，还是想尝试新社交方式的旅行新手。使用场景覆盖从独自出发前寻找旅伴，到在旅途中偶遇同路人，甚至组织小团队探索小众路线，让每一次旅行都变成一场有温度的相遇。