MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality. 
 
Its features include:
<ul>
<li>Automatic data pre-processing, feature engineering and encoding</li>
<li>Classification, regression, time-series tasks</li>
<li>Bring models to production without &ldquo;traditional deployment&rdquo; as AI Tables</li>
<li>Get mModels&rsquo; accuracy scoring and confidence intervals for each prediction</li>
<li>Join ML models with existing data</li>
<li>Anomaly detection</li>
<li>Model explainability analysis</li>
<li>GPU support for models&rsquo; training</li>
<li>Open JSON-AI syntax to build models and bring your own ML blocks in a declarative way</li>
<li>REST API available as well</li>
</ul>

MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality. 







MindsDB - In-Database Machine Learning



Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.
 
Ray AI Runtime (AIR) is an open-source toolkit for building ML applications. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more.
 
Ray Core provides a simple and flexible API for building and running your distributed applications. You can often parallelize single machine code with little to zero code changes.
 
With a Ray cluster you can deploy your workloads on AWS, GCP, Azure or on premise. You can also use Ray cluster managers to run Ray on your existing Kubernetes, YARN, or Slurm clusters.

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.


Ray - A unified framework for scaling AI and Python applications

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.

AdaNet - Fast and flexible AutoML with learning guarantees

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Projects

MindsDB - In-Database Machine Learning

Ray - A unified framework for scaling AI and Python applications

AdaNet - Fast and flexible AutoML with learning guarantees

TechStack

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License