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. -
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. -
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. -
-
Surprise
A Python scikit for building and analyzing recommender systems -
Pylearn2
Warning: This project does not have any current developer. See bellow. -
LightFM
A Python implementation of LightFM, a hybrid recommendation algorithm. -
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. -
skflow
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning -
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 -
awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities. -
karateclub
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020) -
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...) -
TrueSkill, the video game rating system
An implementation of the TrueSkill rating system for Python -
adaptive
:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions -
-
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 -
brew
Multiple Classifier Systems and Ensemble Learning Library in Python. -
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. -
bodywork
ML pipeline orchestration and model deployments on Kubernetes, made really easy. -
MLP Classifier
A handwritten multilayer perceptron classifer using numpy. -
OptaPy
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems. -
openskill.py
A faster, open-license alternative to Microsoft TrueSkill -
tfgraphviz
A visualization tool to show a TensorFlow's graph like TensorBoard -
neptune-contrib
This library is a location of the LegacyLogger for PyTorch Lightning. -
Deliver Cleaner and Safer Code - Right in Your IDE of Choice!
* 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?
|
Less time debugging, more time building
sponsored
scoutapm.com
|
Popular Comparisons
|
SaaSHub - Software Alternatives and Reviews
sponsored
www.saashub.com
|
README
Website | Docs | Community Slack | Contribute | Demo
MindsDB ML-SQL Server enables machine learning workflows for the most powerful databases and datawarehouses 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.
If you like our project then we would really 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 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 as:
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 own 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 mModels’ 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 own ML blocks in a declarative way
- REST API available as well
Database Integrations
MindsDB works with most of the SQL and NoSQL databases and data Streams for real-time ML.
| 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. Documentation for our HTTP API can be found at apidocs.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 the 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 give you guidance!
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.