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Optimize AWS Inferentia utilization with FastAPI and PyTorch models on Amazon EC2 Inf1 & Inf2 instances

AWS Machine Learning

If the model changes on the server side, the client has to know and change its API call to the new endpoint accordingly. If you’re using a different AMI (Amazon Linux 2023, Base Ubuntu etc.), script in the fastapi and trace-model folders use this to create Docker images. install the CLI tools by following this guide.

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JustCall vs Aircall: A Comprehensive Comparison in 2023

JustCall

in your office space The JustCall app can be downloaded on your laptop, tablet, or phone, and your account can also be accessed on the Web via your browser, meaning you can access it on the go Highlights of Aircall Aircall doubles up as a modern phone solution for your Sales and Support teams.

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Deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK

AWS Machine Learning

In April 2023, AWS unveiled Amazon Bedrock , which provides a way to build generative AI-powered apps via pre-trained models from startups including AI21 Labs , Anthropic , and Stability AI. Model data is stored on Amazon Simple Storage Service (Amazon S3) in the JumpStart account. or later node.js

APIs 90
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How to Successfully Start A New Communication Channel In A Call Center?

NobelBiz

Key Points CCaaS is paramount to successfully add a new communication channel You must consider the tone, scripts and pace of new channels Your Call Center must track the right KPIs for every new channel How to add a new communication channel in a call center? Integration with your current software (CRM, API etc.) predicted for 2022.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

Each business unit has each own set of development (automated model training and building), preproduction (automatic testing), and production (model deployment and serving) accounts to productionize ML use cases, which retrieve data from a centralized or decentralized data lake or data mesh, respectively.

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Instruction fine-tuning for FLAN T5 XL with Amazon SageMaker Jumpstart

AWS Machine Learning

Prerequisites To get started, all you need is an AWS account in which you can use Studio. Use the following code to point to the location of the data and set up the output location in a bucket in your account: from sagemaker.s3 We only use the unanswerable questions. import S3Downloader # We will use the train split of SQuAD2.0

APIs 88
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Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support

AWS Machine Learning

Prerequisites To use Local Mode in SageMaker Studio applications, you must complete the following prerequisites: For pulling images from Amazon Elastic Container Registry (Amazon ECR), the account hosting the ECR image must provide access permission to the user’s Identity and Access Management (IAM) role.

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