AI & ML
We offer blend of AI, DevOps, and MLOps services designed to help businesses scale their software and machine learning capabilities with speed, security, and efficiency. Our focus is on building production-grade infrastructure that enables faster development, smarter automation, and reliable AI operations.
From continuous integration and deployment pipelines to managing GPU-based AI workloads and monitoring end-to-end systems, we ensure seamless collaboration between development, operations, and data science teams. Whether you’re deploying applications or training deep learning models, our services are structured to maximize productivity and performance.
CI/CD Pipelines for AI & Applications
End-to-end automation for both software and machine learning workflows using tools like Jenkins, GitHub Actions, and AWS CodePipeline.Cloud-Native Environments
Scalable infrastructure setup for app hosting and ML workloads using AWS SageMaker, EKS, EC2, and serverless services.Model Training & Deployment
Distributed training on Kubernetes with GPU support and automated deployment to staging or production environments.Containerization & Orchestration
Docker-based packaging and Kubernetes-based deployment for microservices and ML models.Monitoring & Observability
Real-time logging, monitoring, and alerts using Prometheus, Grafana, and CloudWatch for DevOps and MLOps pipelines.Infrastructure as Code (IaC)
Fully automated cloud provisioning and environment management using Terraform and AWS CloudFormation.
DevOps Toolchains: Docker, Kubernetes, GitLab CI/CD, Jenkins, Ansible
MLOps Platforms: MLFlow, SageMaker Experiments, TFX
Cloud Platforms: AWS, Azure, GCP
ML Frameworks: TensorFlow, PyTorch, Scikit-learn
Scripting & Automation: Python, Bash, YAML
Distributed Training & GPU Management: Volcano Scheduler
Security & Compliance: Role-based access, IAM, secret management