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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning

These SageMaker endpoints are consumed in the Amplify React application through Amazon API Gateway and AWS Lambda functions. To protect the application and APIs from inadvertent access, Amazon Cognito is integrated into Amplify React, API Gateway, and Lambda functions. You access the React application from your computer.

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.

APIs 97
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Amazon SageMaker Automatic Model Tuning now automatically chooses tuning configurations to improve usability and cost efficiency

AWS Machine Learning

Autotune uses best practices as well as internal benchmarks for selecting the appropriate ranges. Gopi Mudiyala is a Senior Technical Account Manager at AWS. He helps customers in the Financial Services industry with their operations in AWS. Autotune will automatically select the hyperparameter ranges on your behalf.

APIs 76
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How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

AWS Machine Learning

as_trt_engine(output_fpath=trt_path, profiles=profiles) gpt2_trt = GPT2TRTDecoder(gpt2_engine, metadata, config, max_sequence_length=42, batch_size=10) Latency comparison: PyTorch vs. TensorRT JMeter is used for performance benchmarking in this project. implement the model and the inference API. model_fp16.onnx gpt2 and predictor.py

APIs 66
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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units. Regulations in the healthcare industry call for especially rigorous data governance. The data science team expected an AI-based automated image annotation workflow to speed up a time-consuming labeling process.

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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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7-Point Audit Checklist for Customer Success Software

ChurnZero

We also recommend creating an Account segment and a Contact segment to QA the fields that are most critical to the software’s performance. We recommend creating the below QA segments which include key fields that are commonly missed on the Account and Contact level.