LLMOps Platform Starter Kit for AWS

ACCELERATE

LLM adoption with cloud-native services

OPTIMIZE

LLM model performance and resource utilization

ENHANCE

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

CLOUD NATIVE ARCHITECTURE

Harness the power of AWS for open-source LLMs

Leverage AWS cloud services to build and deploy open-source LLM solutions with ease. The starter kit seamlessly integrates with AWS SageMaker, enabling efficient model training, deployment, and hosting, while also utilizing services like AWS Lambda for serverless model inference and AWS Glue for streamlined data ingestion and preprocessing.

DATA ENGINEERING PIPELINE

Streamline data processing for LLM initiatives

Process and prepare large volumes of unstructured data, such as PDF documents and text files, for LLM training and inference. The starter kit provides scalable data engineering pipelines built on AWS cloud-native services or Apache Spark on Amazon EMR, enabling efficient data chunking, vectorization, and preprocessing.

MODEL TRAINING & FINE-TUNING

Customize and optimize open-source LLMs

Leverage AWS SageMaker’s transfer learning capabilities 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 customized LLM models.

SCALABLE INFERENCE LAYER

Deploy and scale LLMs effortlessly

Harness the power of AWS SageMaker for deploying and scaling open-source LLM models. The starter kit supports both single-node and cluster deployments, enabling you to optimize performance and handle varying traffic loads efficiently. Leverage AWS SageMaker’s inference pipelines to create complex LLM chains and workflows.

COMPREHENSIVE OBSERVABILITY

Monitor and optimize LLM performance

Gain deep insights into your LLM applications with comprehensive observability capabilities. Leverage AWS CloudWatch for monitoring hardware utilization, log collection, and metrics tracking, while AWS SageMaker Clarify provides advanced model observability and explainability through automated evaluation metrics like GLUE, ROUGE, and BLEU. Data scientists can select relevant datasets, configure evaluation criteria, and run assessments on self-hosted LLMs or services like AWS Bedrock. This end-to-end observability lifecycle enables continuous monitoring, analysis, and optimization of both infrastructure performance and model behavior.

SEAMLESS INTEGRATION

Accelerate adoption within your existing ecosystem

The LLMOps Platform 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.

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.

Industries

Retail

Develop conversational product assistants, intelligent search for e-commerce, and personalized recommendations using open-source LLMs.

A simple line illustration of a building

Manufacturing

Build intelligent troubleshooting assistants for industrial equipment and analyze technical documentation through natural language processing.

Pharma

Leverage LLMs for mining scientific literature, intelligent question-answering for drug discovery, and clinical trial processes.

Insurance

Develop intelligent claim processing systems, policy analysis using LLMs, and natural language interfaces for customer support.

Wealth Management

Create conversational financial advisors, portfolio assistants, and analyze market trends and reports through natural language capabilities.

Our latest innovations in LLMOps

Insights

LLMOps blueprint for closed-source large language models

The rapid growth of generative AI adoption in the coming years, along with increasing scale and complexity of GenAI solutions, will make the role of LLMOps critical. Many early-stage GenAI solutions will ultimately need to migrate to LLMOps platforms.

Insights

Transforming business process automation with retrieval-augmented generation and LLMs

RAG represents a breakthrough for integrating specialized domain data with large language models’ text generation abilities. Overcoming LLM limitations like hallucinations and context windows, RAG provides an affordable, secure, explainable solution.