Production-grade MLOps pipelines for training, tracking, and deploying Large Language Models and predictive algorithms.
Data science should be reproducible. We configure MLflow Tracking Servers to log parameters, metrics, and artifacts for every experiment run. This allows teams to compare model performance over time and rollback to previous versions with a single click.
We integrate MLflow with popular libraries like PyTorch, TensorFlow, and Hugging Face Transformers, ensuring seamless logging of model weights and hyperparameters.
Generic models like GPT-4 are powerful, but domain-specific tasks require fine-tuning. We build pipelines for Parameter-Efficient Fine-Tuning (PEFT) using techniques like LoRA (Low-Rank Adaptation) and QLoRA on your private data.
For deployment, we utilize high-throughput serving engines like vLLM or TGI (Text Generation Inference) to serve models with minimal latency and optimized GPU memory usage (PagedAttention).
Transitioning a model from staging to production is a critical step. Our workflows strictly control model promotion, requiring passing automated test suites (evaluations) and manual approvals. This ensures only validated models serve live traffic.