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VibE: A Visual Analytics Workflow for Semantic Error Analysis of CVML Models at Subgroup Level Apple Machine Learning Research

​Effective error analysis is critical for the successful development and deployment of CVML models. One approach to understanding model errors is to summarize the common characteristics of error samples. This can be particularly challenging in tasks that utilize unstructured, complex data such as images, where… Read More »VibE: A Visual Analytics Workflow for Semantic Error Analysis of CVML Models at Subgroup Level Apple Machine Learning Research

Advanced Q&A Features with DistilBERT Muhammad Asad Iqbal Khan MachineLearningMastery.com

​This post is divided into three parts; they are: • Using DistilBERT Model for Question Answering • Evaluating the Answer • Other Techniques for Improving the Q&A Capability BERT (Bidirectional Encoder Representations from Transformers) was trained to be a general-purpose language model that can understand… Read More »Advanced Q&A Features with DistilBERT Muhammad Asad Iqbal Khan MachineLearningMastery.com

Amazon Bedrock Guardrails image content filters provide industry-leading safeguards, helping customer block up to 88% of harmful multimodal content: Generally available today Satveer Khurpa AWS Machine Learning Blog

​[[{“value”:” Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. Previously limited to text-only filtering, this enhancement now provides comprehensive content moderation across both modalities. This new capability removes… Read More »Amazon Bedrock Guardrails image content filters provide industry-leading safeguards, helping customer block up to 88% of harmful multimodal content: Generally available today Satveer Khurpa AWS Machine Learning Blog

Integrating custom dependencies in Amazon SageMaker Canvas workflows Nadhya Polanco AWS Machine Learning Blog

​[[{“value”:” When implementing machine learning (ML) workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that… Read More »Integrating custom dependencies in Amazon SageMaker Canvas workflows Nadhya Polanco AWS Machine Learning Blog

Generate training data and cost-effectively train categorical models with Amazon Bedrock Sumeet Kumar AWS Machine Learning Blog

​[[{“value”:” In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase… Read More »Generate training data and cost-effectively train categorical models with Amazon Bedrock Sumeet Kumar AWS Machine Learning Blog

Enable Amazon Bedrock cross-Region inference in multi-account environments Satveer Khurpa AWS Machine Learning Blog

​[[{“value”:” Amazon Bedrock cross-Region inference capability that provides organizations with flexibility to access foundation models (FMs) across AWS Regions while maintaining optimal performance and availability. However, some enterprises implement strict Regional access controls through service control policies (SCPs) or AWS Control Tower to adhere to… Read More »Enable Amazon Bedrock cross-Region inference in multi-account environments Satveer Khurpa AWS Machine Learning Blog