LLMOps

Manage your GenAI deployments combining LLMOps with DevOps

From model training and deployment to monitoring and security, our expertise ensures optimal performance and adherence to data privacy regulations. Let our AI experts guide you on the path to success.

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LLMOps Excellence: Streamlining AI

from Data to Deployment at A21.ai

At A21.ai’s, we help our client with a comprehensive suite of services for the LLM applications.

Our offerings begin with Exploratory Data Analysis (EDA), where we iteratively explore, share, and prepare data, ensuring reproducibility and shareability. This includes transforming, aggregating, and de-duplicating data, along with storing and embedding enterprise data using models like OpenAI API.

In model deployment, we build LLM chains and pipelines, integrating multiple LLM calls and external systems for efficient user service. We also focus on fine-tuning models using popular open-source libraries, ensuring rigorous testing, training, and governance. Managing the model lifecycle from inception to deployment, we use platforms like MLflow and employ CI/CD tools for automating preproduction.

Additionally, we emphasize model monitoring and observability, creating pipelines to detect model drift and malicious behavior, ensuring optimal performance and security.

Our offerings in LLMOPS

Model Training

Well defined methodology for data preparation, model design and setting up the infra

Model Deployment

Including hosting, integration with other services, and scaling mechanisms to handle different loads based on demand

Monitoring

Monitoring the performance, optimizing resources and addressing issues during operation of the model at runtime

Updating and learning

Updation of the model with new data or improved algorithm while mitigating issues like data drift

Security

Ensuring the model adheres to data privacy regulations and guidelines and also protect model from threats

User feedback

Mechanisms for collecting and incorporating user feedback into model improvements

Cost management

Estimating and managing the costs associated with training, deploying, and maintaining the model

Documentation

Providing comprehensive documentation for users and developers to ensure best practices adherence

Model deployment methodology

Building LLM Chains & Pipelines

We help our client by –

  • Building LLM pipelines using tools like LangChain or LlamaIndex
  • LLM stringing with multiple LLM calls and/or calls to external systems such as vector databases or web search
  • Triaging requests to combination of proprietary and open-source models to manage wide variance in query complexity and a need to serve users economically. 

Model Testing, Training & Fine-Tuning

For that right performance, we help our clients by Fine-Tuning using popular open-source libraries such as Hugging Face Transformers, DeepSpeed, PyTorch, TensorFlow, and JAX to improve model performance.

Model Review and Governance

To ensure that the model output meets company AI guidelines, we help –
  • Track model, pipeline lineage, versions, and manage those artifacts and transitions through their lifecycle.
  • Discover, share, and collaborate across ML models with the help of an open-source MLOps platform such as MLFlow.

Model Inference and Serving

To keep the costs and performance optimized we help our clients by – 
  • Managing the frequency of model refresh, inference request times and similar production specifics in testing and QA.
  • Automating the preproduction pipeline by deploying CI/CD tools such as repos and orchestrators (borrowing DevOps principles)

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