MindsDB Alternatives

Similar projects and alternatives to MindsDB

  • GitHub repo tensorflow

    MindsDB VS tensorflow

    An Open Source Machine Learning Framework for Everyone

  • GitHub repo H2O

    MindsDB VS 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.

  • Scout APM

    Scout APM: A developer's best friend. Try free for 14-days. Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.

  • GitHub repo scikit-learn

    MindsDB VS scikit-learn

    scikit-learn: machine learning in Python

  • GitHub repo Keras

    MindsDB VS Keras

    Deep Learning for humans

  • GitHub repo xgboost

    MindsDB VS 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

  • GitHub repo gym

    MindsDB VS gym

    A toolkit for developing and comparing reinforcement learning algorithms.

  • GitHub repo CapRover

    MindsDB VS CapRover

    Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids

  • Nanos

    Run Linux Software Faster and Safer than Linux with Unikernels.

  • GitHub repo bodywork

    MindsDB VS bodywork

    ML pipeline orchestration and model deployments on Kubernetes, made really easy.

  • GitHub repo Monica

    MindsDB VS Monica

    Personal CRM. Remember everything about your friends, family and business relationships.

  • GitHub repo lightwood

    MindsDB VS lightwood

    Lightwood is Legos for Machine Learning.

  • GitHub repo nitroml

    MindsDB VS nitroml

    NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.

  • GitHub repo benchmarks

    Public dataset benchmarks used for measuring the performance of MindsDB. (by mindsdb)

  • GitHub repo MLflow

    MindsDB VS MLflow

    Open source platform for the machine learning lifecycle

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better MindsDB alternative or higher similarity.

Suggest an alternative to MindsDB

Reviews and mentions

Posts with mentions or reviews of MindsDB. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-19.
  • Release0.3 Using PyParing lib in mindsdb
    1 project | dev.to | 29 Nov 2021
    MindsDB is a predictive platform that makes databases intelligent and machine learning easy to use. It allows data analysts to build and visualize forecasts in BI dashboards without going through the complexity of ML pipelines, all through SQL. It also helps data scientists to streamline MLOps by providing advanced instruments for in-database machine learning and optimize ML workflows through a declarative JSON-AI syntax. I did 3 issues of this project, 1773 and 1771 have merged, and 1777 is reviewing.
  • Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database
    6 projects | news.ycombinator.com | 19 Feb 2021
    Here's an issue that enumerates all pending tasks for a first iteration of this feature: https://github.com/mindsdb/mindsdb/issues/1116
    6 projects | news.ycombinator.com | 19 Feb 2021
    Hi HN,

    Adam and Jorge here, and today we’re very excited to share MindsDB with you (http://github.com/mindsdb/mindsdb). MindsDB AutoML Server is an open-source platform designed to accelerate machine learning workflows for people with data inside databases by introducing virtual AI tables. We allow you to create and consume machine learning models as regular database tables.

    Jorge and I have been friends for many years, having first met at college. We have previously founded and failed at another startup, but we stuck together as a team to start MindsDB. Initially a passion project, MindsDB began as an idea to help those who could not afford to hire a team of data scientists, which at the time was (and still is) very expensive. It has since grown into a thriving open-source community with contributors and users all over the globe.

    With the plethora of data available in databases today, predictive modeling can often be a pain, especially if you need to write complex applications for ingesting data, training encoders and embedders, writing sampling algorithms, training models, optimizing, scheduling, versioning, moving models into production environments, maintaining them and then having to explain the predictions and the degree of confidence… we knew there had to be a better way!

    We aim to steer you away from constantly reinventing the wheel by abstracting most of the unnecessary complexities around building, training, and deploying machine learning models. MindsDB provides you with two techniques for this: build and train models as simply as you would write an SQL query, and seamlessly “publish” and manage machine learning models as virtual tables inside your databases (we support Clickhouse, MariaDB, MySQL, PostgreSQL, and MSSQL. MongoDB is coming soon.) We also support getting data from other sources, such as Snowflake, s3, SQLite, and any excel, JSON, or CSV file.

    When we talk to our growing community, we find that they are using MindsDB for anything ranging from reducing financial risk in the payments sector to predicting in-app usage statistics - one user is even trying to predict the price of Bitcoin using sentiment analysis (we wish them luck). No matter what the use-case, what we hear most often is that the two most painful parts of the whole process are model generation (R&D) and/or moving the model into production.

    For those who already have models (i.e. who have already done the R&D part), we are launching the ability to bring your own models from frameworks like Pytorch, Tensorflow, scikit-learn, Keras, XGBoost, CatBoost, LightGBM, etc. directly into your database. If you’d like to try this experimental feature, you can sign-up here: (https://mindsdb.com/bring-your-own-ml-models)

    We currently have a handful of customers who pay us for support. However, we will soon be launching a cloud version of MindsDB for those who do not want to worry about DevOps, scalability, and managing GPU clusters. Nevertheless, MindsDB will always remain free and open-source, because democratizing machine learning is at the core of every decision we make.

    We’re making good progress thanks to our open-source community and are also grateful to have the backing of the founders of MySQL & MariaDB. We would love your feedback and invite you to try it out.

    We’d also love to hear about your experience, so please share your feedback, thoughts, comments, and ideas below. https://docs.mindsdb.com/ or https://mindsdb.com/

    Thanks in advance,

    6 projects | news.ycombinator.com | 19 Feb 2021
    I would love to support Scylla, I ** love that database, those guys are magicians. And I assume in supporting that we'd also offer de-facto support for Cassandra.

    I don't think either Scylla or dynamo are on the roadmap now, but if you want them feel free to create an issue asking for them: https://github.com/mindsdb/mindsdb

    It should be noted that there's two level of support:

    1. As a source of data (easy to implement)

  • MindsDB - build and deploy Machine Learning models from inside your databases in minutes using plain SQL.
    1 project | reddit.com/r/AutoML | 9 Feb 2021


Basic MindsDB repo stats
5 days ago

mindsdb/mindsdb is an open source project licensed under GNU General Public License v3.0 only which is an OSI approved license.

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