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Milvus-Lite Export to JSON
milvus-lite dump -d XavierEdgeAI.db -p
/home/nvidia/nvme/AIMXavierEdgeAI/backup/ -c XavierEdgeAI
Dump collection XavierEdgeAI's data: 100%|████████████████|
33/33 00000000, 188.54it/s]
Dump collection XavierEdgeAI success
Dump collection XavierEdgeAI's data: 100%|████████████████|
33/33 00000000, 127.16it/s]
(milvusvenv)
https://github.com/milvus-io/milvus-lite](https://image.slidesharecdn.com/unstructureddataprocessingfromcloudtoedgewebinar1-240801183249-db301371/75/Unstructured-Data-Processing-from-Cloud-to-Edge-Webinar-31-2048.jpg)
Uploaded byTimothy Spann
Unstructured Data Processing from Cloud to Edge Webinar
The document discusses unstructured data processing and the role of vector databases, particularly Milvus, in managing diverse data types like text, images, and videos. It highlights use cases for edge AI applications, integrates edge devices, and outlines the benefits of employing vector databases for efficient data management, search capabilities, and operational scalability. Additionally, it presents deployment options, technological features, and provides examples of practical applications in various industries.
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Unstructured Data Processing from Cloud to Edge Webinar
- 1. 1 | ©Copyright 2024 Zilliz 1 Unstructured Data Processing From Cloud to Edge Tim Spann @ Zilliz
- 2. 2 | ©Copyright 10/22/23 Zilliz 2 | © Copyright 10/22/23 Zilliz 2 | © Copyright 10/22/23 Zilliz 2 | © Copyright 10/22/23 Zilliz Tim Spann Principal Developer Advocate, Zilliz tim.spann@zilliz.com https://www.linkedin.com/in/timothyspann/ https://x.com/PaaSDev Speaker
- 3. 3 | ©Copyright 2024 Zilliz 3 01 Introduction CONTENTS 02 Edge AI Use Cases 03 Edge Devices
- 4. 4 | ©Copyright 2024 Zilliz 4
- 5. 5 | ©Copyright Zilliz 5 01 Introduction
- 6. 6 | ©Copyright 2024 Zilliz 6 ● Introduction to Unstructured Data Processing ● Introduction to Milvus ● Adding Milvus to Your Infrastructure ● AI Use Case Improvements ● Edge devices working with AI and vector databases
- 7. 7 | ©Copyright Zilliz 7 - Unstructured Data is 80% of data - Vector Databases are the only type of database that can work with unstructured data - Examples of Unstructured Data include text, images, videos, audio, etc Unstructured Data?
- 8. 8 | ©Copyright Zilliz 8 Why Even Use a Vector DB? Beyond High-Performance Search • CRUD Operations: Just like traditional databases, vector databases allow you to Create, Read, Update, and Delete data. • Data Freshness: Vector databases ensure your data remains up-to-date, reflecting the latest information for accurate searches. • Persistence: Your data is securely stored and persists even if the system restarts. • Availability: Your data is readily accessible for search and retrieval operations. • Scalability: Vector databases can handle growing data volumes efficiently.
- 9. 9 | ©Copyright Zilliz 9 Complete Data Management • Data Management: Vector databases provide tools to manage your data effectively, including data ingestion, indexing, and querying. • Backup and Migration: Create backups of your data for disaster recovery and easily migrate your data between different systems. Why Even Use a Vector DB?
- 10. 10 | ©Copyright Zilliz 10 Operational ease • Cloud or On-Premise Deployment: Vector databases can be deployed easily on various platforms, including cloud and on-premise environments. • Observability: Monitor the health and performance of your vector database to ensure optimal operation. • Multi-tenancy: Support multiple users or applications accessing the same database instance securely. Why Even Use a Vector DB?
- 11. 11 | ©Copyright Zilliz 11 Milvus Features Multi-Tenancy Hardware- Accelerated Compute Support Python, Java, Golang, NodeJS Milvus Lite, K8, Zilliz Cloud, Docker Scalable and Elastic Architecture Diverse Index Support Versatile Search Capabilities Tunable Consistency
- 12. 12 | ©Copyright Zilliz 12 Technologies for various types of Use cases Compute Types Designed for various compute powers, such as AVX512, Neon for SIMD, quantization cache-aware optimization and GPU Leverage strengths of each hardware type, ensuring high-speed processing and cost-effective scalability for different application needs Search Types Support multiple types such as top-K ANN, Range ANN, sparse & dense, multi-vector, grouping, and metadata filtering Enable query flexibility and accuracy, allowing developers to tailor their information retrieval needs Multi-tenancy Enable multi-tenancy through collection and partition management Allow for efficient resource utilization and customizable data segregation, ensuring secure and isolated data handling for each tenant Index Types Offer a wide range of 15 indexes support, including popular ones like Hierarchical Navigable Small Worlds HNSW, PQ, Binary, Sparse, DiskANN and GPU index Empower developers with tailored search optimizations, catering to performance, accuracy and cost needs
- 13. 13 | ©Copyright Zilliz 13 02 Edge AI Use Cases
- 14. 14 | ©Copyright Zilliz 14 • Robots • Smart Cities • Smart Factories • Autonomous Cars • Automated Retail • Smart Home Edge AI Use Cases
- 15. 15 | ©Copyright Zilliz 15 • Proprietary Document Search • On-Device Object Detection • Milvus Lite on Device Local Search on Edge Devices
- 16. 16 | ©Copyright Zilliz 16 • 5G and Everywhere Networks • IoT with Cheap Plentiful Sensors • Edge Computing Power CPU, GPU, RAM • Edge Neural Networks and Gen AI • Unstructured Data Processing and Vector DB Edge Hyper Enablers
- 17. 17 | ©Copyright Zilliz 17 • Vision to Images and Videos • Audio from Cameras and Microphones • Raw Text • Edge Neural Networks and Gen AI • Unstructured Data Processing and Vector DB Edge Unstructured Data
- 18. 18 | ©Copyright Zilliz 18 03 Edge Devices
- 19. 19 | ©Copyright Zilliz 19 Demos
- 20. 20 | ©Copyright Zilliz 20 Demos
- 21. 21 | ©Copyright Zilliz 21 • NVIDIA Jetson Xavier NX • NVIDIA Jetson AGX Orin • Smart AI Cameras • Raspberry Pi 5 with Hailo AI Acceleration module Edge Hardware Examples
- 22. 22 | ©Copyright Zilliz 22 • Cloud, Docker, Standalone or On-Premise Deployment: Can send vectors and other fields to local, remote or Cloud Milvus. • Instant Local Search: access local unstructured data for fast search and local applications. • Secure Local Data • No Network Necessary: Especially for autonomous robots and vehicles. Make instant local decisions. • Local RAG and Super Charge Edge AI: enhance local image, audio, video, text data with local LLMs. OLLAMA with RPI. Generative AI • Local Live Video Why Even Use a Vector DB on the Edge?
- 23. 23 | ©Copyright Zilliz 23 Milvus Lite Locally pip install pymilvus
- 24. 24 | ©Copyright Zilliz 24 Milvus Docker Locally or Edge Server pip install pymilvus
- 25. 25 | ©Copyright Zilliz 25 Milvus Zilliz Cloud pip install pymilvus
- 26. 26 | ©Copyright Zilliz 26 Python SDK Connect…
- 27. 27 | ©Copyright Zilliz 27 Some Other SDKs and Interfaces Node.JS Java Golang RESTful API .NET C# Apache Spark Apache Kafka Ruby
- 28. 28 | ©Copyright Zilliz 28 Edge AI Edge Vector Database Retrieval Augmented Generation RAG Run local LLM like OLLAMA Image Similarity Search Capture and search images at the edge for no network, local robotics, remote and secure. Video Similarity Search Search for similar videos, scenes, or objects from local videos. Audio Similarity Search Find similar audios in local audio for tasks like genre classification or speech recognition for robotics and sensing Anomaly Detection Detect data points, events, audio, images and observations that deviate significantly from the usual pattern at the edge Facial Recognition For security applications Customization Robots Benefits Lower latency Offline Security Localized storage
- 29. 29 | ©Copyright Zilliz 29 Edge Vectors to the Cloud Framework Hardware Infrastructure Embedding Models LLMs Software Infrastructure Vector Database
- 30. 30 | ©Copyright Zilliz 30 Milvus-Lite to the Cloud ● Milvus-Lite Dump/Export to Cloud Import ● Dual Ingest ● Switch to Cloud Only ● Kafka / Pulsar / MQTT ● Unstructured Data to MinIO, S3 or Cloud Object Storage
- 31. 31 | ©Copyright Zilliz 31 Milvus-Lite Export to JSON milvus-lite dump -d XavierEdgeAI.db -p /home/nvidia/nvme/AIMXavierEdgeAI/backup/ -c XavierEdgeAI Dump collection XavierEdgeAI's data: 100%|████████████████| 33/33 00000000, 188.54it/s] Dump collection XavierEdgeAI success Dump collection XavierEdgeAI's data: 100%|████████████████| 33/33 00000000, 127.16it/s] (milvusvenv) https://github.com/milvus-io/milvus-lite
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- 34. 34 | ©Copyright Zilliz 34 | © Copyright Zilliz 34 RESOURCES
- 35. 35 | ©Copyright Zilliz 35 Source Code https://github.com/tspannhw/AIM-RPIAIKit-PoseEstimation
- 36. 36 | ©Copyright Zilliz 36 More Source Code https://github.com/tspannhw/AIM-RPIAIKit
- 37. 37 | ©Copyright Zilliz 37 Even More Source Code https://github.com/tspannhw/AIM-XavierEdgeAI
- 38. 38 | ©Copyright Zilliz 38 Vector Database Resources Give Milvus a Star! Chat with me on Discord! https://github.com/milvus-io/milvus
- 39. 39 | ©Copyright Zilliz 39 https://zilliz.com/learn/generative-ai
- 40. Extracting Value fromUnstructured Data Example • A company has 100,000s+ pages of proprietary documentation to enable their staff to service customers. Problem • Searching can be slow, inefficient, or lack context. Solution • Create internal chatbot with ChatGPT and a vector database enriched with company documentation to provide direction and support to employees and customers. https://osschat.io/chat
- 41. We provide deploymentflexibility for different operational, security and compliance requirements BRING YOUR OWN CLOUD Zilliz BYOC Enterprise-ready Milvus for Private VPCs Deploy in your virtual private cloud Zilliz Cloud Milvus Re-engineered for the Cloud Available on the leading public clouds FULLY MANAGED SERVICE Coming Soon! Coming Soon! Milvus Most widely-adopted open source vector database Self hosted on any machine with community support SELF MANAGED SOFTWARE Local Docker K8s
- 42. 42 | ©Copyright Zilliz 42 Well-connected in LLM infrastructure to enable RAG use cases Framework Hardware Infrastructure Embedding Models LLMs Software Infrastructure Vector Database
- 43. 43 | ©Copyright 10/22/23 Zilliz 43 | © Copyright 10/22/23 Zilliz milvus.io github.com/milvus-io/ @milvusio @paasDev /in/timothyspann Connect with me! Thank you!
- 44. 44 | ©Copyright 10/22/23 Zilliz 44 | © Copyright 10/22/23 Zilliz Join the Milvus Discord!
- 45. 45 | ©Copyright 10/22/23 Zilliz 45 | © Copyright 10/22/23 Zilliz 45 | © Copyright 10/22/23 Zilliz 45 | © Copyright 10/22/23 Zilliz Milvus Open Source Self-Managed Zilliz Cloud SaaS Fully-Managed github.com/milvus-io/milvus Getting Started with Vector Databases zilliz.com/cloud
- 46. 46 Unstructured Data Meetup https://www.meetup.com/unstructured-data-meetup-new-york/ Thismeetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs. This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
- 47. 47 | ©Copyright 10/22/23 Zilliz 47 | © Copyright 10/22/23 Zilliz 47 This week in Milvus, Towhee, Attu, GPT Cache, Gen AI, LLM, Apache NiFi, Apache Flink, Apache Kafka, ML, AI, Apache Spark, Apache Iceberg, Python, Java, Vector DB and Open Source friends. https://bit.ly/32dAJft https://github.com/milvus-io/milvus AIM Weekly by Tim Spann
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- 53. 53 | ©Copyright Zilliz 53 T H A N K Y O U