3) Streaming Streaming is a process of generating predictions for a stream of data as it arrives. This is in contrast to batch prediction, where the entire dataset is processed before any predictions are generated.
MACHINE LEARNING
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Real-Time Model Inference Streaming for Low-Latency AI Applications
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Streaming is typically used when the data is arriving in real time, or when the latency requirements are very low. A typical example of this kind of model inference is the social media feeds, movie recsys, etc.
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Batch Prediction: When to Use Large-Scale Data Forecasting
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Batch prediction is typically used when the number of data points to be predicted is large, or when the latency requirements are not as stringent as for online prediction.
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Batch Prediction: Generating Multiple AI Predictions Simultaneously
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2) Batch prediction Batch prediction is a process of generating predictions for a set of data points in one go. This is in contrast to online prediction, where predictions are generated for individual data points as they arrive.
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Three ML Model Deployment Types for Engineers
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3 Types of ML model deployments to learn about as an ML engineer Web-service
Batch prediction
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ML Models as Web Services: TensorFlow and PyTorch Serving
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1) Web-service In this ML models are served as web services by packaging them in a format that can be consumed by a web service framework, such as TensorFlow Serving or PyTorch Serving.
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Model Deployment in Production: MLOps Week 4 Overview
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Week 4 of MLOps Zoom camp ends with the exciting topic of model deployment Let's talk about how models are deployed in prod. under various scenarios
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ViT and MobileViT Computational Performance Benchmark for CVPR
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just the original ViT and MobileViT via timm. It was for a computational performance benchmark (preparing sth for CVPR)
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Fine-tuning Vision Transformers shows promising results
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I'd say so! Recently finetuned a couple of ViTs and it worked pretty well actually.
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AI Research Highlights: LLM Optimizers and Vision Transformer Scaling Laws
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It's been a another wild month in AI & Deep Learning research. I curated and summarized noteworthy papers here: https://
magazine.sebastianraschka.com/p/ai-research-
highlights-in-3-sentences-2a1/
… Ranging from new optimizers for LLMs to new scaling laws for vision transformers.