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LLMOps Process

  • Writer: G B
    G B
  • Feb 15
  • 2 min read



Our LLMOps (Large Language Model Operations) is an end-to-end deployment, monitoring, and optimization of LLMs in production. It combines MLOps principles with LLM-specific challenges, such as data governance, prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).




Step 1 - Start with automating data pipelines


using Apache Airflow and Apache Spark which will enable efficient orchestration and scalable data processing. Airflow schedules and manages workflows, triggering Spark jobs for ETL tasks across distributed data sources. To ensure version control, tools like DVC (Data Version Control) track dataset changes, while Git manages code and pipeline configurations. Integrating CI/CD with GitHub Actions or Jenkins ensures seamless deployment, enabling reproducible and auditable ML workflows. 🚀






Step 2 - Fine-Tune open source transformers


to develop LLM's using techniques like PEFT, LORA/QLORA. Implemented techniques like PyMuPDF, pdfplumber, PDFMiner, PyMuPDF, pdfplumber, Whisper to extract and prepare data for LLM training.





Step 3 - Deploying a model


using MLflow Model Registry ensures version control and easy management of different model stages (staging, production). Once registered, the model is packaged into a Docker container, enabling a portable and scalable deployment. The containerized model is then deployed using Kubernetes, where pods handle inference requests efficiently. Kubernetes automates scaling, load balancing, and monitoring, ensuring a robust production-grade ML system. 🚀






Step 4 - Automating model deployment is key


Use CI/CD with GitHub Actions ensures seamless integration from MLflow Model Registry to containerization and Kubernetes deployment. A GitHub Actions workflow can be triggered on model updates, automatically building a Docker image, pushing it to a container registry (e.g., Docker Hub, AWS ECR), and deploying it to Kubernetes using Helm or Kubectl. This enables continuous deployment with version control, rollback capabilities, and minimal manual intervention. 🚀




 
 
 

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