DevOps & MLOps

What We Deliver

• CI/CD pipelines — Automated testing, building, security scanning, and deployment pipelines for modern engineering teams.
• Kubernetes & container orchestration — Production-grade clusters designed with autoscaling, zero-downtime deployments, and GitOps workflows.
• Cloud-native architecture — AWS, GCP, or Azure infrastructure designed for scale, cost-efficiency, and operational clarity.
• Monitoring, alerting & observability — Metrics, logs, traces, uptime checks, dashboards, and alerting through tools like Grafana, Prometheus, and OpenTelemetry.
• ML model training pipelines — Automated data ingestion, preprocessing, training, validation, and model versioning using best practices.
• Model serving & deployment — High-availability model APIs using tools such as TensorRT, TorchServe, BentoML, Seldon, or custom Rust/Go inference services.
• Feature stores & data pipelines — Centralized, versioned feature management for both batch and real-time workloads.
• ML experiment tracking — Track metrics, versions, parameters, and artifacts using MLflow, Weights & Biases, or custom systems.

Why It Matters

Modern teams need fast, stable, and automated deployments. Whether you're shipping applications or machine learning models, DevOps and MLOps workflows eliminate downtime, improve engineering velocity, and create predictable, repeatable releases.

Benefits

• Faster and safer deployments
• Lower operational overhead
• Automated ML training and inference workflows
• Consistent reproducibility of experiments and results
• Shorter iteration cycles for engineering and data science
• Better reliability, security, and visibility