Artwork

A tartalmat a Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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[Paid Course] Snowpal Education: (Weaviate) Open Source Vector Database

1:31
 
Megosztás
 

Manage episode 456056998 series 3530865
A tartalmat a Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.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

198 epizódok

Artwork
iconMegosztás
 
Manage episode 456056998 series 3530865
A tartalmat a Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Krish Palaniappan and Varun Palaniappan, Krish Palaniappan, and Varun Palaniappan vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.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

198 epizódok

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