Real time Object Detection and Analytics using RedisEdge and Docker
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The document discusses real-time object detection and analytics using the RedisAI stack and Docker, emphasizing the role of AI databases in optimizing machine learning model training and serving. It introduces RedisAI as a powerful tool for AI inferencing, capable of supporting various deep learning frameworks and operating efficiently across diverse environments. The document also outlines the operational pipeline and the benefits of using Docker containers in AI deployments.
Real time Object Detection and Analytics using RedisEdge and Docker
- 1. Real Time Object Detection & Analytics using Docker & RedisEdge Stack
- 2. - Blogger @ Collabnix - Docker Captain - DevRel at Redis Labs - Docker Community Leader - ARM Innovator - Worked in Dell, VMware & CGI $whoami @ajeetsraina www.collabnix.com
- 3. - The Rise of AI Database - A Typical AI Data Pipeline - Challenges with AI Serving Platform - Why Redis & RedisAI? - Why Docker? - Overview of RedisEdge Stack - Demo Agenda Target Audience: MLOps & DevOps
- 4. “The hardest part of AI is not AI; it’s data”.
- 5. - A database built with the sole purpose of speeding up Machine Learning (ML) model training as well as model serving. - AI databases simultaneously ingest, explore, analyze, and visualize fast-moving, complex data within milliseconds - Helps you better wrangle the volume, velocity and complex data governance and management challenges associated with training ML and Deep Learning models to save time and optimize resources. - Comes with lower costs, integrate ML models so that businesses can make more efficient, data-driven decisions. What is an AI Database?
- 6. Data Sources Data Ingestion Data Preparation Model Training Model Serving High Performance Transient Centralized IoT Business Processes Data Pre-Processing Trained Model Deep Learning Framework Deploy Trained Model Inference AI Pipeline is data intensive
- 7. AI Pipeline ~ A Closer View
- 8. Data Sources Data Ingestion Data Preparation Model Training Model Serving Data VelocityData Variety Data Volume Data Quality Data Access Latency Data Caching Response Time Throughput AI Pipeline Characteristics
- 9. Challenges with existing AI Serving Platform - Slower End to End Inferencing - Lack of HA and Downtime - Lack of Platform Agnostic Solution - Complexity in deploying multiple models
- 11. Introducing RedisAI - It is a Redis module - Provide tensor as a data type - Turns Redis into a full-fledge Deep learning Model Execution - Runs on CPUs and GPUs Data Structure: - Tensor - Model - Script DL/ML Backends: - Tensorflow - PyTorch - ONNX - TensorRT $ docker run -p 6379:6379 -it --rm redisai/redisai
- 12. Benefits of RedisAI - AI inferencing where your data lives - Deploy new models with no downtime or performance penalties - Serve AI over a robust, scalable, and production-proven platform - Superior performance - Built-in support for all major AI backends - Runs everywhere
- 13. RedisEdge Redis OSS Streams Modules - Bundles Open Source Redis + Redis Streams + Redis Modules - A Multi-model database built for IoT Edge - Can ingest millions of writes per second with <1ms latency - A Small footprint (<5MB), easily resides in constrained compute environments. - Runs on a variety of edge devices and sensors ranging from ARM32 to x64-based hardware.
- 16. Around 94% of AI Adopters are using or plan to use containers within 1 year time. Source: 451 Research
- 17. Running RedisEdge using Docker Compose Build on Open Source init - A Service that initializes Redis with the RedisAI model, RedisTimeSeries downsampling rules and the RedisGears gear. capture - A Service that captures video stream frames from a webcam or image/video file and stores it in a Redis Stream. Server - A web server that serves a rendered image composed of the raw frame and the model's detections.
- 18. Running RedisEdge using Docker Compose Build on Open Source Grafana + Redis Application python init.py --device GPU With GPU
- 19. Wake Up ! It’s Demo Time
- 20. - https://github.com/collabnix/Redisai_edgeAnalytics - https://github.com/redisai/redisai - https://redislabs.com/modules/redis-ai/ References
- 21. Thank You