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AI code-generation software: What it is and how it works

IBM Journey to AI blog

Using generative artificial intelligence (AI) solutions to produce computer code helps streamline the software development process and makes it easier for developers of all skill levels to write code. It can also modernize legacy code and translate code from one programming language to another.

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Managing Computer Vision Projects with Micha? Tadeusiak 

The MLOps Blog

Also, science projects around technologies like predictive modeling, computer vision, NLP, and several profiles like commercial proof of concepts and competitions workshops. When we speak about like NLP problems or classical ML problems with tabular data when the data can be spread in huge databases. This is a much harder thing.

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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.

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Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint

AWS Machine Learning Blog

The added benefit of asynchronous inference is the cost savings by auto scaling the instance count to zero when there are no requests to process. SageMaker features and capabilities help developers and data scientists get started with natural language processing (NLP) on AWS with ease.

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Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning Blog

This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion and token level log probabilities).

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Announcing provisioned concurrency for Amazon SageMaker Serverless Inference

AWS Machine Learning Blog

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.

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Improve performance of Falcon models with Amazon SageMaker

AWS Machine Learning Blog

The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The decode phase is responsible for completing the text to make it coherent and grammatically correct. The default is 32.