Operationalize AI & Data with Confidence
Robust, scalable, and secure cloud infrastructure is the backbone of modern AI. DevDot designs, builds, and manages production-grade environments to accelerate your data initiatives.
The Foundation for AI Success
Stable, observable, and efficient infrastructure to power your models and data pipelines.
Scalability & Reliability
Built for Growth & Uptime
We design cloud architectures that scale effortlessly with your data volume and user load. Leverage auto-scaling, load balancing, and resilient design patterns.
Ensure high availability and zero-downtime deployments for your critical AI and data applications.
Speed & Efficiency
Accelerate Your Workflow
Streamline your development lifecycle with automated CI/CD pipelines for code, data, and models. Implement Infrastructure as Code (IaC) for reproducible environments.
Reduce manual effort, minimize errors, and get your innovations to market faster.
Observability & Cost Control
Monitor Performance & Spend
Gain complete visibility into your systems with integrated logging, monitoring (Prometheus, Grafana), and alerting. Track model performance, data drift, and infrastructure health.
Optimize cloud resource utilization and manage costs effectively with clear reporting and FinOps best practices.
Infrastructure & MLOps Solutions
From foundational cloud setup to sophisticated MLOps pipelines.
Model Serving Infrastructure
Deploy ML models as scalable REST/gRPC endpoints using tools like FastAPI, KFServing, or cloud-native services (SageMaker Endpoints, Vertex AI). Includes A/B testing & shadow deployment setup.
ML CI/CD Pipelines
Automated pipelines for building, testing, and deploying ML models. Integrates data validation, model training, evaluation, and versioning (DVC, MLflow) using GitHub Actions, GitLab CI, or Jenkins.
Cloud Data Platforms
Design and setup of scalable data lakes (S3, ADLS, GCS), data warehouses (Snowflake, Redshift, BigQuery), and vector databases (Pinecone, Qdrant) optimized for AI workloads.
Our Cloud & MLOps Process
A structured approach to building reliable and scalable AI infrastructure.
Assess & Design Architecture
We analyze your current state, requirements (scalability, security, cost), and workload characteristics to design a tailored cloud architecture and MLOps strategy using Infrastructure as Code (IaC) principles.
Build & Automate Pipelines
Provisioning cloud resources using Terraform/CloudFormation. Building automated CI/CD pipelines for code, container images, infrastructure changes, and ML model training/deployment.
Deploy & Integrate Monitoring
Deploying applications and models into production environments (Kubernetes, SageMaker, etc.). Integrating robust monitoring, logging (Prometheus, Grafana, CloudWatch), and alerting systems.
Optimize & Handover
Performance tuning, cost optimization analysis, security hardening, and final system validation. Providing comprehensive documentation and training for your team's successful handover.
Ready to Streamline Your AI Operations?
Let's discuss how robust Cloud & MLOps infrastructure can accelerate your AI initiatives and ensure production success.
Cloud & MLOps Outcomes
Operational excellence from infrastructure to model serving.
↓45%
Infra spend via rightsizing & autoscaling
↑99.9%
CI/CD pipeline success for data & models
≤15m
P1 incident response with on-call runbooks
↑6×
Faster model deploys using feature stores
Engagement Models
Choose the path that fits your timeline and risk profile.
Fixed-Scope Packages
Kubernetes/Serverless deploy, IaC baseline, observability stack.
Sprint-Based
Model CI/CD, feature store build, canary releases & rollbacks.
Dedicated Squad
SRE + MLOps on-call, SLOs, cost governance & capacity planning.
Cloud & MLOps FAQs
Common questions about our infrastructure services.
Let's Build The Future, Together.
Have a project in mind or just want to explore possibilities? Drop us a line. We provide a no-obligation proposal with a clear timeline and transparent pricing.