|5 days ago||3 days ago|
|Apache License 2.0||GNU General Public License v3.0 only|
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.
[PAID] Looking for Phaser.js game developer
1 project | reddit.com/r/INAT | 9 Dec 2021
Built and founded various web3 projects for last 2 years such as OpenArt and 8RealmDojo for last 2 years as well as being high performing student in CTU in Prague and SeoulTech. Was offered internships in Amazon and H2O.ai. Created robots assistants using robots from SoftBank.
Enabling predictive capabilities in ClickHouse database
2 projects | dev.to | 16 Dec 2021
Try making your own predictions with MindDB yourself, by simply signing up for a free cloud account or installing it via Docker. If you need any help feel free to throw a question in the MindsDB community via Slack or Github.
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
Here's an issue that enumerates all pending tasks for a first iteration of this feature: https://github.com/mindsdb/mindsdb/issues/1116
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.
Thanks in advance,
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
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
scikit-learn - scikit-learn: machine learning in Python
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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
CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.