Kamran Razavi
12 papers 53 citations h-index 4 2020–2026
Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems
The use of machine learning (ML) inference for various applications is growing drastically. ML inference services engage with users directly, requiring fast and accurate responses. Moreover, these...
Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling
Mobile and IoT applications increasingly adopt deep learning inference to provide intelligence. Inference requests are typically sent to a cloud infrastructure over a wireless network that is...
IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency
Efficiently optimizing multi-model inference pipelines for fast, accurate, and cost-effective inference is a crucial challenge in machine learning production systems, given their tight end-to-end...
Operator as a Service: Stateful Serverless Complex Event Processing
Complex Event Processing (CEP) is a powerful paradigm for scalable data management that is employed in many real-world scenarios such as detecting credit card fraud in banks. The so-called complex...
FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees
IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency
A Tale of Two Scales: Reconciling Horizontal and Vertical Scaling for Inference Serving Systems
Inference serving is of great importance in deploying machine learning models in real-world applications, ensuring efficient processing and quick responses to inference requests. However, managing...
NetNN: Neural Intrusion Detection System in Programmable Networks
The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs in the network...