Your machine learning models need to scale for real-time data. Can you meet the challenge?
When your machine learning models need to handle real-time data, it's crucial to ensure they can process and adapt quickly. Here's how to meet the challenge:
What strategies do you use to scale machine learning models for real-time data?
Your machine learning models need to scale for real-time data. Can you meet the challenge?
When your machine learning models need to handle real-time data, it's crucial to ensure they can process and adapt quickly. Here's how to meet the challenge:
What strategies do you use to scale machine learning models for real-time data?
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This involves optimising model architecture and deployment to handle incoming data streams with minimal latency necessitating efficient computational design. Techniques such as batch processing where micro batches are utilised can enhance performance. Leveraging distributed systems and parallel processing frameworks like Apache Kafka or Apache Flink ensures seamless data ingestion and preprocessing in real time. Light weight architectures and quantisation and use of accelerators can optimise model inference speed. Pruning and knowledge distillation can be used to reduce computational overhead.