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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

AWS Machine Learning

With this format, we can easily query the feature store and work with familiar tools like Pandas to construct a dataset to be used for training later. For this we use AWS Step Functions , a serverless workflow service that provides us with API integrations to quickly orchestrate and visualize the steps in our workflow.

Scripts 87
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Zero-shot text classification with Amazon SageMaker JumpStart

AWS Machine Learning

The framework works by posing the sequence to be classified as an NLI premise and constructs a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class politics , we could construct a hypothesis of “This text is about politics.”

Scripts 73
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Everything You Need to Know About Custom Web Development

OctopusTech

Every undertaking, whether constructing a developing app or website, requires thorough planning. Developers may construct efficient web designs using front-end coding and front-end languages. The template-driven websites need more ability for customization because they contain an excessive amount of unnecessary, long scripts.

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Fine-tune text-to-image Stable Diffusion models with Amazon SageMaker JumpStart

AWS Machine Learning

Today, we announce that you can personalize the image generation model to your use case by fine-tuning it on your custom dataset in Amazon SageMaker JumpStart. Fine-tuning large models like Stable Diffusion usually requires you to provide training scripts. training_instance_type = "ml.g4dn.2xlarge"

Scripts 76
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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Omni-channel communication also offers the flexibility to tailor interactions based on the nature of the debt and debtor preferences, leading to more personalized and effective collection efforts. This level of personalization can significantly enhance the effectiveness of collection efforts.

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Achieve high performance with lowest cost for generative AI inference using AWS Inferentia2 and AWS Trainium on Amazon SageMaker

AWS Machine Learning

You can use ml.inf2 and ml.trn1 instances to run your ML applications on SageMaker for text summarization, code generation, video and image generation, speech recognition, personalization, fraud detection, and more. xlarge" ) Refer to Developer Flows for more details on typical development flows of Inf2 on SageMaker with sample scripts.

APIs 74
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Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

AWS Machine Learning

You can access Amazon Comprehend document analysis capabilities using the Amazon Comprehend console or using the Amazon Comprehend APIs. Further personalize your experience with the adjustable warm light, font sizes, line spacing, and more. The CMMH building will be the second building constructed by the UP in the UST.