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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

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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

However, as a new product in a new space for Amazon, Amp needed more relevant data to inform their decision-making process. Part 1 shows how data was collected and processed using the data and analytics platform, and Part 2 shows how the data was used to create show recommendations using Amazon SageMaker , a fully managed ML service.

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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

With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB. For more information on processing jobs, see Process data.

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Contact Center Trends 2024: Our Predictions

Fonolo

We live in an era of big data, AI, and automation, and the trends that matter in CX this year begin with the abilities – and pain points – ushered in by this technology. For example, big data makes things like hyper-personalized customer service possible, but it also puts enormous stress on data security.

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Customer Experience Automation: Transforming the Future of Customer Service

TechSee

Today, CXA encompasses various technologies such as AI, machine learning, and big data analytics to provide personalized and efficient customer experiences. Moreover, advanced analytics capabilities built into these platforms allow businesses to monitor customer sentiment and track performance metrics in real time.

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

AWS Machine Learning

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. 1 with the following additions: The Snowflake Connector for Python to download the data from the Snowflake table to the training instance.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications. This approach was not only time-consuming but also prone to errors and difficult to scale.