| 1 - What is Vespa.mp4 | 4.1 MB | ||
| 10 - Introduction to Dataset.mp4 | 2.8 MB | ||
| 10 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 10 - vespa-standalone-final-2.py | 12.1 KB | ||
| 11 - Load Dataset.mp4 | 27.8 MB | ||
| 11 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 11 - vespa-standalone-final-2.py | 12.1 KB | ||
| 12 - Convert Dataset to Vespas Format.mp4 | 10.5 MB | ||
| 12 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 12 - vespa-standalone-final-2.py | 12.1 KB | ||
| 13 - Create Application Package.mp4 | 21.6 MB | ||
| 13 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 13 - vespa-standalone-final-2.py | 12.1 KB | ||
| 14 - Method 1 Create Vespa Cloud Instance using Interactively.mp4 | 10.9 MB | ||
| 14 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 14 - vespa-standalone-final-2.py | 12.1 KB | ||
| 15 - Method 2 Create Vespa Cloud Instance using PEM FileAutomatically.mp4 | 36.4 MB | ||
| 15 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 15 - vespa-standalone-final-2.py | 12.1 KB | ||
| 16 - Deployment of Vespa Application.mp4 | 56.9 MB | ||
| 16 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 16 - vespa-standalone-final-2.py | 12.1 KB | ||
| 17 - Feed Data to Vespa Application.mp4 | 17.6 MB | ||
| 17 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 17 - vespa-standalone-final-2.py | 12.1 KB | ||
| 18 - Display Function to Pretty Display Result as DataFrame.mp4 | 30.8 MB | ||
| 18 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 18 - vespa-standalone-final-2.py | 12.1 KB | ||
| 19 - Plain Keyboard Search.mp4 | 33.7 MB | ||
| 19 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 19 - vespa-standalone-final-2.py | 12.1 KB | ||
| 2 - Key Features of Vespa.mp4 | 11.4 MB | ||
| 20 - Plain Semantic Search.mp4 | 35 MB | ||
| 20 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 20 - vespa-standalone-final-2.py | 12.1 KB | ||
| 21 - Hybrid Search with OR Query Operator.mp4 | 13.2 MB | ||
| 21 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 21 - vespa-standalone-final-2.py | 12.1 KB | ||
| 22 - Hybrid Search with RANK Query Operator.mp4 | 8.9 MB | ||
| 22 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 22 - vespa-standalone-final-2.py | 12.1 KB | ||
| 23 - Hybrid Search with Filter.mp4 | 9.2 MB | ||
| 23 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 23 - vespa-standalone-final-2.py | 12.1 KB | ||
| 24 - Update Content of Deployed Application.mp4 | 64.5 MB | ||
| 24 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 24 - vespa-standalone-final-2.py | 12.1 KB | ||
| 25 - Reconnect with Vespa Application using PEM Files.mp4 | 26.3 MB | ||
| 25 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 25 - vespa-standalone-final-2.py | 12.1 KB | ||
| 3 - Low Level Overview of Vespa.mp4 | 7.5 MB | ||
| 4 - Architecture of Vespa.mp4 | 10.6 MB | ||
| 5 - What is Tenant.mp4 | 4.9 MB | ||
| 6 - Login to Vespa Cloud and Create Tenant.mp4 | 6.9 MB | ||
| 6 - Vespa AI.txt | 0 B | ||
| 7 - Vespa Cloud Overview.mp4 | 8 MB | ||
| 8 - Install pyvespa vespacli datasets.mp4 | 47.3 MB | ||
| 8 - Notebook.txt | 102.4 B | ||
| 8 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 8 - vespa-standalone-final-2.py | 12.1 KB | ||
| 9 - Load Dependencies.mp4 | 8.6 MB | ||
| 9 - Notebook.txt | 102.4 B | ||
| 9 - vespa-standalone-final-2.ipynb | 70.9 KB | ||
| 9 - vespa-standalone-final-2.py | 12.1 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 66 total files | |||
Vespa AI Search Engine and Vector Database with Python
https://DevCourseWeb.com
Published 8/2024
Duration: 1h22m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 516 MB
Genre: eLearning | Language: English
Build search engines and vector databases with Vespa AI. Master Python integration, data processing, and ML techniques.
What you'll learn
Understand Vespa AI: Learn the fundamentals of Vespa AI to build and deploy powerful search engines and vector databases effectively.
Build Search Applications: Create advanced search applications with Vespa AI using Python, focusing on real-time data processing and retrieval.
Develop Vector Databases: Learn to develop, deploy, and manage vector databases with Vespa AI, enhancing search with machine learning models.
Integrate Vespa AI with Python: Gain practical skills to integrate Vespa AI into Python projects, from deploying applications to scaling for real-world use case
Requirements
Prerequisites: Basic knowledge of Python programming and familiarity with Google Colab are required to follow along with the course exercises and examples.
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