AI with Redis
Run your deep learning models where the data lives
It’s a lot easier to figure out how to store your data than it is to extract insights from it. The required depth and speed of analysis often inhibit solutions. While artificial intelligence (AI) allows you to go deep, today’s tools and architectures limit result speed for even the best of queries. Traditional databases are inherently slow and batch processing analytics engines require you to distribute data and model execution across multiple machines.
Now there’s a better way to solve this problem. RedisAI seamlessly integrates deep learning and machine learning models and algorithms with your data in Redis. By unifying traditionally siloed data collection and model execution environments, RedisAI simplifies and accelerates time to deliver AI models by executing them close to where the data resides.
Bring AI models to your data
Co-locate your data with model execution in memory to extract value in real-time
Prepare high-volume data in real time
Automatically classify, transform and make real-time adjustments to send only relevant data to serve your AI models at scale
Load different AI models dynamically
Embed popular models into your analytical workflows using built-in integrations with TensorFlow, PyTorch and TorchScrip
How it Works
Integrate artificial intelligence with your data for real-time insights. Serving tensors and execute machine learning and deep learning models in-memory in Redis.
Run AI models where your data lives in-memory, in Redis. Eliminate data bottlenecks and wasted CPU cycles on serializing/deserializing. Accelerate your time to insight using GPUs and in-memory computing for dynamic parallel processing and fast query execution.
Accelerated deep learning for applications
Enrich your data with ease, using new data types such as Tensor, Scripts and Models. Switch between models, manipulate your input and output data, run your feature pipeline on your input data and perform complex operations with ease.
Dynamic artificial intelligence
Solve your complex, compute-intensive data analytics by combining your data in Redis with AI libraries from TensorFlow, PyTorch and TorchScript. Easily extend and operationalize machine learning and deep learning workloads.
Become familiar with the RedisAI module and learn how best to use it in your environment.
Get Started Right Away
Check out RedisAI module, in preview, for serving tensors and executing deep learning graphs.
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