Google Professional Machine Learning Engineer Question 356
Select 4Google Cloud PlatformYou are deploying a machine learning model on Google Cloud to process user queries in real-time. The application requires low latency and high throughput to handle thousands of queries per second. During testing, you notice significant latency spikes and memory usage issues. Which steps should you take to optimize the performance of your ML solution?
- A
Use Vertex AI Prediction with GPU accelerators to reduce inference latency for large models.
- B
Enable model quantization to reduce the model size and improve inference speed.
- C
Switch to preemptible VMs to reduce costs, even if it increases latency.
- D
Implement asynchronous batch prediction for handling high-throughput real-time queries.
- E
Optimize the model by reducing the number of layers or parameters that do not significantly impact performance.
- F
Use caching mechanisms for frequently requested predictions to reduce redundant computations.
Show answer and explanation
Correct answers: A, B, E, F
Explanation
To optimize an ML solution for real-time, low-latency, high-throughput scenarios, you need to balance hardware acceleration, model optimization, and system-level optimizations. GPU accelerators and model quantization can significantly improve inference speed. Simplifying the model architecture reduces computation requirements. Caching frequently requested predictions avoids redundant computation. Preemptible VMs and asynchronous batch prediction are not suitable for real-time requirements as they prioritize cost or throughput over latency.
- A. Correct.
Using GPU accelerators in Vertex AI Prediction can significantly reduce inference latency, especially for large models, making it suitable for real-time applications.
- B. Correct.
Model quantization reduces the size of the model and speeds up inference by using lower precision (e.g., INT8 instead of FP32), without significantly impacting accuracy.
- C. Incorrect.
Preemptible VMs are cost-effective but not ideal for low-latency, real-time applications because they can be interrupted at any time, causing delays.
- D. Incorrect.
Asynchronous batch prediction is designed for non-real-time workloads where high throughput is required, but it is not suitable for low-latency real-time applications.
- E. Correct.
Reducing the number of layers or parameters in the model can improve inference speed and reduce memory usage while maintaining acceptable performance.
- F. Correct.
Caching predictions for frequently requested inputs can reduce redundant computations, improving both latency and throughput for common queries.