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AI Transparency and the Need for Open-Source Models

Unite.AI

In order to protect people from the potential harms of AI, some regulators in the United States and European Union are increasingly advocating for controls and checks and balances on the power of open-source AI models. When AI models become observable, they instill confidence in their reliability and accuracy.

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The AI Feedback Loop: Maintaining Model Production Quality In The Age Of AI-Generated Content

Unite.AI

Production-deployed AI models need a robust and continuous performance evaluation mechanism. This is where an AI feedback loop can be applied to ensure consistent model performance. But, with the meteoric rise of Generative AI , AI model training has become anomalous and error-prone.

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Five open-source AI tools to know

IBM Journey to AI blog

The diversity and accessibility of open-source AI allow for a broad set of beneficial use cases, like real-time fraud protection, medical image analysis, personalized recommendations and customized learning. This availability makes open-source projects and AI models popular with developers, researchers and organizations.

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Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker

AWS Machine Learning Blog

Organizations are looking to accelerate the process of building new AI solutions. They use fully managed services such as Amazon SageMaker AI to build, train and deploy generative AI models. Oftentimes, they also want to integrate their choice of purpose-built AI development tools to build their models on SageMaker AI.

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Snorkel Flow 2023.R3 release: PaLM integration, streamlined onboarding, and enhanced user experience

Snorkel AI

This new guided workflow is designed to ensure success for your AI use case, regardless of complexity, catering to both seasoned data scientists and those just beginning their journey. While creating your app, you’ll receive a preview of your dataset, allowing you to identify and correct critical data errors early.

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Snorkel Flow 2023.R3 release: PaLM integration, streamlined onboarding, and enhanced user experience

Snorkel AI

This new guided workflow is designed to ensure success for your AI use case, regardless of complexity, catering to both seasoned data scientists and those just beginning their journey. While creating your app, you’ll receive a preview of your dataset, allowing you to identify and correct critical data errors early.

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Snorkel AI Teams with Google Cloud and Vertex AI to speed AI deployment

Snorkel AI

This time-consuming, labor-intensive process is costly – and often infeasible – when enterprises need to extract insights from volumes of complex data sources or proprietary data requiring specialized knowledge from clinicians, lawyers, financial analysis or other internal experts.