MindsDB
postgresml
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MindsDB | postgresml | |
---|---|---|
77 | 23 | |
20,270 | 5,345 | |
2.6% | 3.0% | |
10.0 | 9.7 | |
5 days ago | 1 day ago | |
Python | Rust | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MindsDB
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How to Forecast Air Temperatures with AI + IoT Sensor Data
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
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Fine-tuning a Mistral Language Model with Anyscale
Make sure you install the requirements for the Anyscale Endpoints integration.
MindsDB is an open-source AI platform for developers that connects AI/ML models with real-time data. It provides tools and automation to easily build and maintain personalized AI solutions.
- Vanna.ai: Chat with your SQL database
- FLaNK Weekly 08 Jan 2024
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MindsDB Docker Extension: Build ML powered apps at a much faster pace
MindsDB combines both AI and SQL functions in one; users can create, train, optimize, and deploy ML models without the need for external tools. Data analysts can create and visualize forecasts without having to navigate the complexities of ML pipelines.MindsDB is open-source and works with well-known databases like MySQL, Postgres, Redit, Snowflakes, etc.
MindsDB Project Repo
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How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
Mindsdb is a good example. It abstracts everything related to an AI workflow as "virtual tables". For example, you can import OpenAI API as a "virtual table":
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ππ 23 issues to grow yourself as an exceptional open-source Python expert π§βπ» π₯
Repo : https://github.com/mindsdb/mindsdb
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AI-Powered Selection of Asset Management Companies using MindsDB and LlamaIndex
MindsDB is an AI Automation platform for building AI/ML powered features and applications. It works by connecting any data source with any AI/ML model or framework and automating how real-time data flows between them. MindsDB is integrated with LlamaIndex, which makes use of its data framework for connecting custom data sources to large language models. LlamaIndex data ingestion allows you to connect to data sources like PDFβs, webpages, etc., provides data indexing and a query interface that takes input prompts from your data and provides knowledge-augmented responses, thus making it easy to Q&A over documents and webpages.
postgresml
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Pg_later: Asynchronous Queries for Postgres
I don't think you'd replace a materialized view with pg_later, but it might help you populate or update your materialized view if you are trying to do that asynchronously. pglater.exec() works with DDL too!
I use it a lot for long running queries when doing data science and machine learning work, and a lot of times when executing queries from a jupyter notebook or CLI. That way if my jupyter kernel dies, my query execution continues even if the network or my environment has an issue. I've started using it a bit more with https://github.com/postgresml/postgresml for model training tasks too, since those can be quite long running depending on the situation.
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Replace pinecone.
PostgresML comes with pgvector as a vector database. The cool thing is it can run your models in the same memory space as a database extension. Weβre also working on ggml support for huggingface transformers, but could use some help testing more LLMs for compatibility. https://github.com/postgresml/postgresml/pull/748
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Show HN: We unified LLMs, vector memory, ranking, pruning models in one process
[1]: https://huggingface.co/spaces/mteb/leaderboard
[2]: https://postgresml.org/blog/generating-llm-embeddings-with-open-source-models-in-postgresml
[3]: https://postgresml.org/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database
[4]: https://postgresml.org/blog/personalize-embedding-vector-search-results-with-huggingface-and-pgvector
Github: https://github.com/postgresml/postgresml
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PostgresML is 8-40x faster than Python HTTP microservices
in the prediction code to begin with: https://github.com/postgresml/postgresml/blob/15c8488ade86b0...
To get any real insights, you'd have to benchmark every single line of the prediction function (called "api") to see where the slowdown is actually coming from https://github.com/postgresml/postgresml/blob/15c8488ade86b0...
Everything else is just speculation.
- Show HN: PostgresML, now with analytics and project management
- Show HN: PostgresML, end-to-end machine learning in your favorite db
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
lightwood - Lightwood is Legos for Machine Learning.
scikit-learn - scikit-learn: machine learning in Python
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
MLflow - Open source platform for the machine learning lifecycle
Keras - Deep Learning for humans
blynk - Blynk is an Internet of Things Platform aimed to simplify building mobile and web applications for the Internet of Things. Easily connect 400+ hardware models like Arduino, ESP8266, ESP32, Raspberry Pi and similar MCUs and drag-n-drop IOT mobile apps for iOS and Android in 5 minutes
gym - A toolkit for developing and comparing reinforcement learning algorithms.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
bodywork - ML pipeline orchestration and model deployments on Kubernetes.