Description
MindsDB is an Explainable AutoML framework for developers built on top of Pytorch. It enables you to build, train and test state of the art ML models in as simple as one line of code.
MindsDB alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view MindsDB alternatives based on common mentions on social networks and blogs.
-
tensorflow
An Open Source Machine Learning Framework for Everyone -
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 -
gym
A toolkit for developing and comparing reinforcement learning algorithms. -
CNTK
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit -
PaddlePaddle
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署) -
Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. -
TFLearn
Deep learning library featuring a higher-level API for TensorFlow. -
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. -
NuPIC
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex. -
Surprise
A Python scikit for building and analyzing recommender systems -
LightFM
A Python implementation of LightFM, a hybrid recommendation algorithm. -
Pylearn2
Warning: This project does not have any current developer. See bellow. -
skflow
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning -
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. -
Clairvoyant
Software designed to identify and monitor social/historical cues for short term stock movement -
python-recsys
A python library for implementing a recommender system -
Metrics
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave -
karateclub
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020) -
awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities. -
Crab
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). -
seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) -
adaptive
:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions -
Xorbits
Scalable Python DS & ML, in an API compatible & lightning fast way. -
TrueSkill, the video game rating system
An implementation of the TrueSkill rating system for Python -
SciKit-Learn Laboratory
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments. -
rwa
Machine Learning on Sequential Data Using a Recurrent Weighted Average -
Feature Forge
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API -
Data Flow Facilitator for Machine Learning (dffml)
The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease. -
brew
Multiple Classifier Systems and Ensemble Learning Library in Python. -
MLP Classifier
A handwritten multilayer perceptron classifer using numpy. -
OptaPy
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems. -
omega-ml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle -
ChaiPy
A developer interface for creating advanced chatbots for the Chai app. -
tfgraphviz
A visualization tool to show a TensorFlow's graph like TensorBoard
Learn any GitHub repo in 59 seconds
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of MindsDB or a related project?
Popular Comparisons
README
Website | Docs | Community Slack | Contribute | Demo | Hacktoberfest
MindsDB ML-SQL Server enables machine learning workflows for the most powerful databases and data warehouses using SQL.
- Developers can quickly add AI capabilities to your applications.
- Data Scientists can streamline MLOps by deploying ML models as AI Tables.
- Data Analysts can easily make forecasts on complex data (like multivariate time-series with high cardinality) and visualize them in BI tools like Tableau.
NEW! Check-out the new Hacktoberfest challenges (and the cash:dollar: , laptop:computer: and many other prizes)!
If you like our project then we would truly appreciate a Star ⭐!
Also, check-out the rewards and community programs.
Installation - Overview - Features - Database Integrations - Quickstart - Documentation - Support - Contributing - Mailing lists - License
Machine Learning using SQL
Demo
You can try the Mindsdb ML SQL server here (demo).
Installation
To install the latest version of MindsDB please pull the following Docker image:
docker pull mindsdb/mindsdb
Or, use PyPI:
pip install mindsdb
Overview
MindsDB automates and abstracts machine learning models through virtual AI Tables:
Apart from abstracting ML models as AI Tables inside databases, MindsDB has a set of unique capabilities:
Easily make predictions over very complex multivariate time-series data with high cardinality
An open JSON-AI syntax to tune ML models and optimize ML pipelines in a declarative way
How it works:
Let MindsDB connect to your database.
Train a Predictor using a single SQL statement (make MindsDB learn from historical data automatically) or import your ML model to a Predictor via JSON-AI.
Make predictions with SQL statements (Predictor is exposed as virtual AI Tables). There’s no need to deploy models since they are already part of the data layer.
Check our docs and blog for tutorials and use case examples.
Features
- Automatic data pre-processing, feature engineering, and encoding
- Classification, regression, time-series tasks
- Bring models to production without “traditional deployment” as AI Tables
- Get models’ accuracy scoring and confidence intervals for each prediction
- Join ML models with existing data
- Anomaly detection
- Model explainability analysis
- GPU support for models’ training
- Open JSON-AI syntax to build models and bring your ML blocks in a declarative way
Database Integrations
MindsDB works with most of the SQL and NoSQL databases and data Streams for real-time ML.
Connect your Data | Connect your Data | Connect your Data |
---|---|---|
:question: :wave: Missing integration?
Quickstart
To get your hands on MindsDB, we recommend using the Docker image or simply sign up for a free cloud account. Feel free to browse documentation for other installation methods and tutorials.
Documentation
You can find the complete documentation of MindsDB at docs.mindsdb.com.
Support
If you found a bug, please submit an issue on GitHub.
To get community support, you can:
- Post at MindsDB Slack community.
- Ask for help at our GitHub Discussions.
- Ask a question at Stackoverflow with a MindsDB tag.
If you need commercial support, please contact MindsDB team.
Contributing
A great place to start contributing to MindsDB will be our GitHub projects for checkered_flag:
- Community writers dashboard tasks.
- Community code contributors dashboard tasks.
Also, we are always open to suggestions so feel free to open new issues with your ideas and we can guide you!
Being part of the core team is accessible to anyone who is motivated and wants to be part of that journey! If you'd like to contribute to the project, refer to the contributing documentation.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project, you agree to abide by its terms.
Current contributors
Made with contributors-img.
Mailing lists
Subscribe to MindsDB Monthly Community Newsletter to get general announcements, release notes, information about MindsDB events, and the latest blog posts. You may also join our beta-users group, and get access to new beta features.
License
MindsDB is licensed under GNU General Public License v3.0
*Note that all licence references and agreements mentioned in the MindsDB README section above
are relevant to that project's source code only.