What you should know

Contents

• Introduction Introduction

  • Learning AI-native vector databases

    50s
  • (Locked)
    What you should know

    45s
  • The superpower of vector databases

    2m 7s
• 1. Data: What Data Do Vector Databases Store and How Is It Stored? 1. Data: What Data Do Vector Databases Store and How Is It Stored?

  • (Locked)
    Structured versus unstructured data

    2m 49s
  • Human-understandable versus machine-understandable data

    3m 35s
  • (Locked)
    Drawing out and visualizing vector representations of data

    3m 40s
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    Introduce the concept of distance between two vectors

    2m 20s
  • (Locked)
    Challenge: Working with vectors

    51s
  • Solution: Working with vectors

    16m 16s
• 2. Natural Querying: How Do You Search for Data in a Vector Database? 2. Natural Querying: How Do You Search for Data in a Vector Database?

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    Frame the query as a question or search

    1m 56s
  • (Locked)
    Generate the question in machine-understandable language

    1m 22s
  • Adding data to a vector database

    9m 48s
  • Performing semantic searches using Weaviate

    13m 36s
  • (Locked)
    Challenge: Vector search with Weaviate

    49s
  • Solution: Vector Search with Weaviate

    11m 5s
• 3. Machine Learning Vectors: How Does a Vector Database Understand Your Data? 3. Machine Learning Vectors: How Does a Vector Database Understand Your Data?

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    Machine learning models and object classification

    2m 2s
  • (Locked)
    Translating data from human to machine-understandable

    2m 11s
  • ML models and vector embeddings

    15m 52s
  • (Locked)
    Challenge: Search with images and text

    51s
  • Solution: Search with images and text

    14m 28s
• 4. Scalability: What Does a Vector Database Need to Do? 4. Scalability: What Does a Vector Database Need to Do?

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    Scalability: When to use a vector DB

    2m 43s
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    Ways to measure performance of a vector DB

    2m 26s
  • CRUD operations in vector DBs

    9m 58s
  • (Locked)
    Challenge: CRUD and performance

    55s
  • Solution: CRUD and performance

    8m 49s
• 5. Demonstrate Vector DBs and Use Cases 5. Demonstrate Vector DBs and Use Cases

  • Vector DB1: E-commerce RecSys

    9m 15s
  • Vector DB2: Hybrid search

    9m 6s
  • (Locked)
    Vector DB3: Retrieval augmented generation

    9m 6s
  • Challenge: Vector DBs

    1m
  • (Locked)
    Solution: Vector DBs

    6m 12s
• Conclusion Conclusion

  • Continue your AI-native vector databases learning journey

    55s