Updating a vector database and doing a new training run for Rafiki are two distinct processes with different purposes:
Updating vector database: This involves adding, removing, or modifying existing vector embeddings in the database. This process is typically fast and efficient, allowing for real-time updates to the data.
New training run: This involves re-training Rafiki's machine learning model on a new dataset or updated data, which can include changes to the vector database. This process is more computationally intensive and time-consuming, as it requires re-training the entire model.
Key Differences
The key differences between the two processes are:
Scope: Updating the vector database only affects the specific data being updated, whereas a new training run affects the entire model and its performance.
Purpose: Updating the vector database is used to reflect changes to the data, whereas a new training run is used to improve the model's performance, adapt to new patterns, or incorporate new knowledge.
Frequency: Vector database updates can occur frequently, even in real-time, whereas new training runs are typically done less frequently, such as when significant changes to the data or model architecture are made.
Updating Vector Database vs. New Training Run
Updating a vector database and doing a new training run for Rafiki are two distinct processes with different purposes:
Key Differences
The key differences between the two processes are: