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Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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[Paid Course] Snowpal Education: (Weaviate) Open Source Vector Database

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Manage episode 456056998 series 3530865
Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

In this conversation, Krish Palaniappan introduces Weaviate, an open-source vector database, and explores its functionalities compared to traditional databases. The discussion covers the setup and configuration of Weaviate, hands-on coding examples, and the importance of vectorization and embeddings in AI. The conversation also addresses debugging challenges faced during implementation and concludes with a recap of the key points discussed. Takeaways

  • Weaviate is an open-source vector database designed for AI applications.

  • Vector databases differ fundamentally from traditional databases in data retrieval methods.

  • Understanding vector embeddings is crucial for leveraging vector databases effectively.

  • Hands-on coding examples help illustrate the practical use of Weaviate.

  • Python is often preferred for AI-related programming due to its extensive support.

  • Debugging is an essential part of working with new technologies like Weaviate.

  • Vectorization optimizes database operations for modern CPU architectures.

  • Embedding models can encode various types of unstructured data.

  • The conversation emphasizes co-learning and exploration of new technologies.

  • Future discussions may delve deeper into the capabilities of vector databases.

Chapters

00:00 Introduction to Weaviate and Vector Databases

06:58 Understanding Vector Databases vs Traditional Databases

12:05 Exploring Weaviate: Setup and Configuration

20:32 Hands-On with Weaviate: Coding and Implementation

34:50 Deep Dive into Vectorization and Embeddings

42:15 Debugging and Troubleshooting Weaviate Code

01:20:40 Recap and Future Directions

Purchase course in one of 2 ways:

1. Go to https://getsnowpal.com, and purchase it on the Web

2. On your phone:

(i) If you are an iPhone user, go to http://ios.snowpal.com, and watch the course on the go.

(ii). If you are an Android user, go to http://android.snowpal.com.

  continue reading

209 에피소드

Artwork
icon공유
 
Manage episode 456056998 series 3530865
Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

In this conversation, Krish Palaniappan introduces Weaviate, an open-source vector database, and explores its functionalities compared to traditional databases. The discussion covers the setup and configuration of Weaviate, hands-on coding examples, and the importance of vectorization and embeddings in AI. The conversation also addresses debugging challenges faced during implementation and concludes with a recap of the key points discussed. Takeaways

  • Weaviate is an open-source vector database designed for AI applications.

  • Vector databases differ fundamentally from traditional databases in data retrieval methods.

  • Understanding vector embeddings is crucial for leveraging vector databases effectively.

  • Hands-on coding examples help illustrate the practical use of Weaviate.

  • Python is often preferred for AI-related programming due to its extensive support.

  • Debugging is an essential part of working with new technologies like Weaviate.

  • Vectorization optimizes database operations for modern CPU architectures.

  • Embedding models can encode various types of unstructured data.

  • The conversation emphasizes co-learning and exploration of new technologies.

  • Future discussions may delve deeper into the capabilities of vector databases.

Chapters

00:00 Introduction to Weaviate and Vector Databases

06:58 Understanding Vector Databases vs Traditional Databases

12:05 Exploring Weaviate: Setup and Configuration

20:32 Hands-On with Weaviate: Coding and Implementation

34:50 Deep Dive into Vectorization and Embeddings

42:15 Debugging and Troubleshooting Weaviate Code

01:20:40 Recap and Future Directions

Purchase course in one of 2 ways:

1. Go to https://getsnowpal.com, and purchase it on the Web

2. On your phone:

(i) If you are an iPhone user, go to http://ios.snowpal.com, and watch the course on the go.

(ii). If you are an Android user, go to http://android.snowpal.com.

  continue reading

209 에피소드

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