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LLM adoption with cloud-native services


LLM model performance and resource utilization


Observability & governance

Unlock the power of LLMs with a cloud-native starter kit

The rise of open-source LLMs has ushered in a new era of innovation, enabling organizations to develop customized solutions tailored to their unique business requirements. However, the path to successful LLM adoption is often fraught with challenges, including complex data engineering pipelines, scalability concerns, and the need for robust observability and model management capabilities.

The LLMOps Starter Kit for AWS addresses these challenges head-on, leveraging the power of AWS cloud services to provide a comprehensive, cloud-native solution for open-source LLM initiatives. Built on the foundations of AWS SageMaker, this starter kit empowers developers and data scientists to streamline the entire LLM lifecycle, from data preprocessing and model training to deployment, scaling, and observability.

LLMOps starter kit features


Harness the power of AWS for open-source LLMs


Streamline data processing for LLM initiatives


Customize and optimize open-source LLMs


Deploy and scale LLMs effortlessly


Monitor and optimize LLM performance


Accelerate adoption within your existing ecosystem

How the LLMOps starter kit works

The LLMOps Platform Starter Kit for AWS leverages the power of AWS cloud services to provide a comprehensive solution for building and deploying open-source LLM applications. At the core of the starter kit lies AWS SageMaker, a fully managed machine learning service that simplifies the entire LLM lifecycle.

The data engineering pipeline, built on AWS cloud-native services or Apache Spark on Amazon EMR, processes and prepares large volumes of unstructured data for LLM training and inference. This includes data chunking, vectorization, and preprocessing steps, ensuring that your LLM models are trained on high-quality, relevant data.

AWS SageMaker’s transfer learning capabilities enable you to fine-tune open-source LLMs like LLaMA 2 on your domain-specific data, creating accurate and tailored models for your use cases. The starter kit simplifies the process of training, tuning, and deploying these customized LLM models.

For deployment and inference, the starter kit leverages AWS SageMaker’s scalable and efficient infrastructure. You can deploy your LLM models in a single-node or cluster configuration, optimizing performance and handling varying traffic loads effectively. AWS SageMaker’s inference pipelines allow you to create complex LLM chains and workflows, enabling advanced use cases.

Comprehensive observability is achieved through a combination of AWS CloudWatch for monitoring hardware utilization, log collection, and metrics tracking, and AWS SageMaker Clarify for advanced model observability and explainability. Clarify provides automated evaluation metrics like GLUE, ROUGE, and BLEU, enabling you to gain insights into your LLM models’ performance and make data-driven optimizations.

The LLMOps Starter Kit for AWS seamlessly integrates with your existing AWS infrastructure and services, enabling you to leverage your existing investments and accelerate the adoption of open-source LLMs within your organization’s technology ecosystem.


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