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Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

AWS Machine Learning

Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior ML experience. Build your training script for the Hugging Face SageMaker estimator. return tokenized_dataset. to(device).

Scripts 104
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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning

We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw. For example, to use the RedPajama dataset, use the following command: wget [link] python nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py

Scripts 132
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21 Business Analysts & Call Center Leaders Reveal the Optimal Role of the Business Analyst in Call Center Operations

Callminer

Bottom Line: The optimal role of a business analyst in call center operations is to improve the customer service experience by optimizing operations through trend and data analysis and identifying and implementing strategies based on the data to improve efficiencies within the call center. Andrew Tillery. MAPCommInc.

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Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. framework/createmodel/ – This directory contains a Python script that creates a SageMaker model object based on model artifacts from a SageMaker Pipelines training step. script is used by pipeline_service.py The model_unit.py

Scripts 129
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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Sagemaker provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers.

Scripts 136
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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

Batch transform The batch transform pipeline consists of the following steps: The pipeline implements a data preparation step that retrieves data from a PrestoDB instance (using a data preprocessing script ) and stores the batch data in Amazon Simple Storage Service (Amazon S3).

Scripts 130
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. You can find the sample script in GitHub.

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