DevOps Solutions
DevOps for AI/ML Products
AI/ML products need infrastructure that traditional DevOps doesn't cover — GPU provisioning, model training pipelines, experiment tracking, model versioning, and inference serving with auto-scaling. We implement MLOps infrastructure that bridges the gap between your data science notebooks and production model serving.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
MLOps Challenges
ML systems have unique operational requirements: GPU scheduling for training jobs, large dataset management, experiment reproducibility, model versioning and registry, A/B testing of model versions, inference latency optimization, and monitoring for model drift. Traditional CI/CD doesn't cover these. We implement infrastructure that does.
GPU Infrastructure
We provision GPU instances (AWS p4d/g5, GCP A2/L4, Azure NC/ND series) with proper CUDA drivers, container runtimes (NVIDIA Container Toolkit), and Kubernetes device plugins. Spot/preemptible instances with checkpointing reduce training costs by 60–70%. Node pools are segregated so GPU resources are reserved for training and inference workloads.
Training Pipelines
We implement automated training pipelines using Kubeflow, Airflow, or custom Kubernetes jobs. Pipelines pull data from your feature store or data lake, run preprocessing, execute training with hyperparameter sweeps, evaluate model performance, and register successful models in your model registry. Training runs are reproducible with versioned data, code, and configuration.
Model Serving & Inference
Models are served via KServe, Triton Inference Server, TorchServe, or custom FastAPI services depending on your framework. Auto-scaling responds to inference request volume. Model versions are deployed with canary rollouts — new models serve a percentage of traffic while performance is validated. Batch inference jobs handle offline scoring workloads.
Experiment Tracking & Model Registry
MLflow or Weights & Biases tracks experiment parameters, metrics, and artifacts. The model registry stores versioned models with metadata, training lineage, and deployment status. Promotion workflows move models from experimentation to staging to production with appropriate validation gates.
How It Works
Purchase the engagement, submit your async brief with your ML framework, model architecture, and serving requirements, and receive a complete MLOps implementation within 10–14 business days.
Why Anubiz Engineering
100% async — no calls, no meetings
Delivered in days, not weeks
Full documentation included
Production-grade from day one
Security-first approach
Post-delivery support included
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.