Remove Auto-classification Remove ML Engineer Remove Prompt Engineering
article thumbnail

Benchmarking Computer Vision Models using PyTorch & Comet

Heartbeat

Make sure that you import Comet library before PyTorch to benefit from auto logging features Choosing Models for Classification When it comes to choosing a computer vision model for a classification task, there are several factors to consider, such as accuracy, speed, and model size. Pre-trained models, such as VGG, ResNet.

article thumbnail

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development. Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on.

Metadata 134
article thumbnail

Virtual fashion styling with generative AI using Amazon SageMaker 

AWS Machine Learning Blog

This blog post details the implementation of generative AI-assisted fashion online styling using text prompts. Machine learning (ML) engineers can fine-tune and deploy text-to-semantic-segmentation and in-painting models based on pre-trained CLIPSeq and Stable Diffusion with Amazon SageMaker.