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MetaGPT: Complete Guide to the Best AI Agent Available Right Now

Unite.AI

To actualize an agile, flexible software architecture that can adapt to dynamic programming tasks. Agile Development SOPs act as a meta-function here, coordinating agents to auto-generate code based on defined inputs. The post MetaGPT: Complete Guide to the Best AI Agent Available Right Now appeared first on Unite.AI.

Python 328
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Boosting Salesforce Einstein’s code generating model performance with Amazon SageMaker

AWS Machine Learning Blog

Einstein has a list of over 60 features, unlocked at different price points and segmented into four main categories: machine learning (ML), natural language processing (NLP), computer vision, and automatic speech recognition. These models are designed to provide advanced NLP capabilities for various business applications.

LLM 128
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Deploy Falcon-40B with large model inference DLCs on Amazon SageMaker

AWS Machine Learning Blog

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. His expertise lies in Deep Learning in the domains of Natural Language Processing (NLP) and Computer Vision.

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AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

AWS Machine Learning Blog

To get started, complete the following steps: On the File menu, choose New and Terminal. Use CodeWhisperer in Studio After we complete the installation steps, we can use CodeWhisperer by opening a new notebook or Python file. To get started, complete the following steps: On the File menu, choose New and Terminal.

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Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools

AWS Machine Learning Blog

Complete the following steps to edit an existing space: On the space details page, choose Stop space. To start using Amazon CodeWhisperer, make sure that the Resume Auto-Suggestions feature is activated. Majisha Namath Parambath is a Senior Software Engineer at Amazon SageMaker. Choose Create JupyterLab space.

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Configure and use defaults for Amazon SageMaker resources with the SageMaker Python SDK

AWS Machine Learning Blog

Set up the environment To deploy a complete infrastructure including networking and a Studio domain, complete the following steps: Clone the GitHub repository. Provide a name for the stack (for example, networking-stack ), and complete the remaining steps to create the stack. something: '1.0'

Python 103
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Streamline diarization using AI as an assistive technology: ZOO Digital’s story

AWS Machine Learning Blog

This time-consuming process must be completed before content can be dubbed into another language. SageMaker asynchronous endpoints support upload sizes up to 1 GB and incorporate auto scaling features that efficiently mitigate traffic spikes and save costs during off-peak times.

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