MindsDB
Tribuo
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MindsDB | Tribuo | |
---|---|---|
78 | 15 | |
21,223 | 1,218 | |
5.7% | 0.6% | |
10.0 | 5.3 | |
4 days ago | 22 days ago | |
Python | Java | |
GNU General Public License v3.0 or later | Apache 2.0 |
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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Whatβs the Difference Between Fine-tuning, Retraining, and RAG?
Check us out on GitHub.
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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
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.
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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.
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Using Large Language Models inside your database with MindsDB
Now, imagine if you can deploy these highly trained models in your database to get insights, make predictions, understand your users, auto-generate content, and more. MindsDB makes this possible! MindsDB is an open-source AI database middleware that allows you to supercharge your databases by integrating various machine learning (ML) engines.
Tribuo
- FLaNK Weekly 08 Jan 2024
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Is deeplearning4j a good choice?
It seems to have been picked up by Eclipse and there is also Oracle Labs' Tribuo and Deep Java Library. All seem active, but I don't know much about any of them. I agree it's probably best to follow the community and use a more popular tool like PyTorch.
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Stochastic gradient descent written in SQL
We built model & data provenance into our open source ML library, though it's admittedly not the W3C PROV standard. There were a few gaps in it until we built an automated reproducibility system on top of it, but now it's pretty solid for all the algorithms we implement. Unfortunately some of the things we wrap (notably TensorFlow) aren't reproducible enough due to some unfixed bugs. There's an overview of the provenance system in this reprise of the JavaOne talk I gave here https://www.youtube.com/watch?v=GXOMjq2OS_c. The library is on GitHub - https://github.com/oracle/tribuo.
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Just want to vent a bit
Although it may be a bit more work, you can do both machine learning and AI in Java. If you are doing deep learning, you can use DeepJavaLibrary (I do work on this one at Amazon). If you are looking for other ML algorithms, I have seen Smile, Tribuo, or some around Spark.
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Anybody here using Java for machine learning?
We've been developing Tribuo on Github for two years now, MS are very actively developing ONNX Runtime (and the Java layer is fairly thin and wrapped over the same C API they use for node.js and C#), and things like XGBoost and LibSVM have been around for many years and the Java bits are developed in tree with the rest of the code so updated along with it. Amazon have a team of people working on DJL, though you'd have to ask them what their plans are.
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Java engineer wants to be a researcher
FWIW, Oracle actually did release a Java ML library - https://github.com/oracle/tribuo.
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txtai 3.4 released - Build AI-powered semantic search applications in Java
Tribuo (tribuo.org, github.com/oracle/tribuo). ONNX export support is there for 2 models at the moment in main, there's a PR for factorization machines which supports ONNX export, and we plan to add another couple of models and maybe ensembles before the upcoming release. Plus I need to write a tutorial on how it all works, but you can check the tests in the meantime.
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Hottest topics for research for JAVA software engineers
You can do ML & data science in Java (full disclosure: I help run TensorFlow-Java, I maintain ONNX Runtime's Java interface, and I'm the lead developer on Oracle Labs' Java ML library Tribuo, so I'm pretty biased). It tends not to be as favoured in research, though I've published academic ML papers which used Java implementations. People do deploy ML models quite a bit in Java in industry.
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John Snow Labs Spark-NLP 3.1.0: Over 2600+ new models and pipelines in 200+ languages, new DistilBERT, RoBERTa, and XLM-RoBERTa transformers, support for external Transformers, and lots more!
It might be worth having a look at the ONNX Runtime Java API in addition to TF-Java, it'll let you deploy the rest of the HuggingFace pytorch models that don't have TF equivalents. I built the Java API a few years ago, and it's now a supported part of the ONNX Runtime project. We use it in Tribuo to provide one of our text feature embedding classes (BERTFeatureExtractor).
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If it gets better w age, will java become compatible for machine learning and data science?
The IJava notebook kernel works pretty well for data science on top of Java. We use it in Tribuo to write all our tutorials, and if you've got the jar file in the right folder everything is runnable. For example, this is our intro classification tutorial - https://github.com/oracle/tribuo/blob/main/tutorials/irises-tribuo-v4.ipynb.
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
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.
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
oj! Algorithms - oj! Algorithms
CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
spark-nlp - State of the Art Natural Language Processing
scikit-learn - scikit-learn: machine learning in Python
txtai - π‘ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
lightwood - Lightwood is Legos for Machine Learning.
grobid - A machine learning software for extracting information from scholarly documents