What are some effective tools for reducing AI API latency?
There are several tools and techniques to reduce AI API latency, such as caching, load balancing, and using faster data processing frameworks.
Can you explain how caching works in this context?
Caching stores frequently accessed data in memory, allowing for quicker retrieval and reducing the need to fetch data from slower databases.
What about load balancing? How does that help?
Load balancing distributes incoming API requests across multiple servers, ensuring no single server becomes a bottleneck, which improves response times.
Are there specific frameworks you recommend for faster data processing?
Yes, frameworks like TensorFlow, PyTorch, and Apache Spark are popular for their efficiency in handling large datasets and reducing processing time.
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