AWS Machine Learning Blog

MAY 15, 2024

Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). The image is then uploaded into an Amazon Simple Storage Services (Amazon S3) bucket for images and the metadata about the image is stored in an Amazon DynamoDB table (step 6).

Generative AI 90

Generative AI AI AI Machine Learning 90

Sebastian Ruder

NOVEMBER 14, 2022

Writing System and Speaker Metadata for 2,800+ Language Varieties. Chen, D., … Stoyanov, V. Chaudhary, V., Guzmán, F., … Stoyanov, V. Ravishankar, V., Chaudhary, V., Chaudhary, V., Lucassen, T., Caswell, I., & Rivera, C. Proceedings of LREC 2022 , (June), 5035–5046.

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics AI 52

PyImageSearch

MARCH 13, 2023

dropout ratio) and other relevant metadata (e.g., Hub has its own repository and a config.json file (see an example over here. The configuration file defines the model architecture (e.g., number of layers, hidden dimension), hyperparameters (e.g., labels in the dataset). Note: We use XXXModelConfig to make the code more generic. .

Computer Vision 117

Computer Vision Deep Learning Neural Network Metadata 117

AWS Machine Learning Blog

MAY 14, 2024

,AWS SageMaker is a machine learning service that allows you to train and deploy machine learning models in the cloud.","RHLF-custom-feedback":{"workerId":"private.us-east-1.8c185c045aed3bef","result":{"relevance":{"label":"5 RHLF-custom-feedback":{"workerId":"private.us-east-1.8c185c045aed3bef","result":{"relevance":{"label":"5

Generative AI 86

Generative AI LLM Chatbots Machine Learning 86

AWS Machine Learning Blog

MAY 31, 2023

Each model deployed with Triton requires a configuration file ( config.pbtxt ) that specifies model metadata, such as input and output tensors, model name, and platform. docker run --gpus=all --rm -it -v `pwd`/workspace:/workspace nvcr.io/nvidia/pytorch:23.02-py3 Triton uses TorchScript for improved performance and flexibility.

ML 83

ML Auto-classification Auto-complete Natural Language Processing 83

AWS Machine Learning Blog

JUNE 6, 2023

After model training is complete, package the saved model, inference scripts, and a few metadata files into a tar file that SageMaker inference can use and upload the model package to an Amazon Simple Storage Service (Amazon S3) bucket. Run your DLC container with a model training script to fine-tune the RoBERTa model.

ML 69

ML Deep Learning BERT Python 69

Heartbeat

NOVEMBER 8, 2023

This gives you access to metadata like the number of tokens used. '}] generate is similar to apply, except it returns an LLMResult instead of a string. Use this when you want the entire LLMResult object returned, not just the generated text.

LLM 52

LLM OpenAI Data Scientist Prompt Engineer 52

AWS Machine Learning Blog

MAY 8, 2023

Input and output – These fields are required because NVIDIA Triton needs metadata about the model. docker run --gpus=all --rm -it -v `pwd`/workspace:/workspace nvcr.io/nvidia/pytorch:23.02-py3 For models that don’t support batching, or don’t support batching in the specific ways we’ve described, max_batch_size must be set to 0.

ML 83

ML BERT Deep Learning Auto-complete 83

AWS Machine Learning Blog

SEPTEMBER 7, 2023

v1alpha5 kind: ClusterConfig metadata: name: do-eks-yaml-g5 version: "1.25" region: us-east-1 availabilityZones: - us-east-1a - us-east-1b - us-east-1c - us-east-1d managedNodeGroups: - name: sys instanceType: m5.xlarge The EKS API metadata has been combined with the DCGM metrics data, resulting in the provided pod-based GPU metrics.

Metadata 88

Metadata Machine Learning ML Deep Learning 88

AWS Machine Learning Blog

AUGUST 8, 2023

docker run --gpus=all --rm -it -v `pwd`/workspace:/workspace nvcr.io/nvidia/pytorch:22.10-py3 nvidia/pytorch:22.10-py3 py3 /bin/bash generate_model_trt.sh $model_id We then copy the model artifacts to the directory we created earlier and add a version to the path: !

BERT 78

BERT Deep Learning Auto-classification ML 78