RedisAI Documentation
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AI serving engine for real-time applications
AI creates new data-layer challenges for organizations, including handling the proliferation and complexity of inference data. Requiring no added infrastructure, RedisAI allows you to run your inference engine where the data lives, decreasing latency and increasing simplicity—all coupled with the core Redis Enterprise features.
Introduction to RedisAI from RedisConf 2020 keynote
Enriching AI transactions with reference data can be vastly slower than the AI processing itself. RedisAI minimizes latency by retrieving the reference data directly from the database’s shared memory.
Update models transparently without affecting inference performance. Fully integrated with MLFlow for managing your AI lifecycle.
With RedisAI you also get all of Redis Enterprise’s features, including high availability (99.999%) and infinite linear scalability without performance trade-offs.
Delivering up to 9x more throughput than other AI model-serving platforms.
Serve machine-learning and deep-learning models trained by state-of-the-art platforms like TensorFlow, PyTorch, or ONNXRuntime. Run inferences across platforms.
Run on CPUs, state-of-the-art GPUs, high-end compute engines, or even tiny Raspberry Pi or NVIDIA Jetson devices.
Reduce fraud with real-time transaction scoring. Enhanced credit-decision making with reference data at blazing speed.
AI-powered retail analytics and decisions. Drive more revenue with better personalization and AI-powered product recommendations.
RedisAI can leverage time-series data in RedisTimeSeries for forecasting and anomaly detection.
Combine RedisAI with RediSearch to increase search relevancy and create better user experiences.
Use your data to create knowledge graphs and serve them in RedisGraph. These knowledge graphs can give AI models context for better inferencing.
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