DevOps & MLOps
Modern software and AI-enabled applications demand not only high-quality code but also robust automation, scalable infrastructure, and continuous delivery. AiPearlz delivers DevOps and MLOps services that help you streamline operations, accelerate deployments, and maintain reliability across both traditional applications and machine learning systems.
DevOps focuses on automation, collaboration, and operational excellence across software lifecycle stages, while MLOps extends DevOps principles into machine learning model lifecycle management, model deployment, monitoring, retraining, governance, and performance optimization.
One-Line Value Proposition
Implement reliable, scalable, and automated pipelines for both software and machine-learning systems — from code to production, with built-in monitoring, governance, and continuous improvement.
Why DevOps & MLOps Matter
- Faster Releases: Automated CI/CD pipelines reduce deployment times and eliminate manual errors.
- Resilience & Reliability: Infrastructure automation and orchestration minimize downtime and improve uptime.
- Scalable ML Deployments: Models transition smoothly from experimentation to production with monitoring and drift detection.
- Visibility & Governance: Centralized logs, metrics, and governance frameworks support compliance and audit readiness.
- DevOps and MLOps together bring continuous integration, delivery, automation, and lifecycle monitoring to both code and machine learning assets — crucial for modern digital products.
Core Services & Deliverables
- 1. DevOps Implementation
- CI/CD Pipeline Architecture: End-to-end continuous integration and continuous delivery for software and services.
- Infrastructure as Code (IaC): Terraform, CloudFormation, Pulumi to define and version infrastructure.
- Containerization & Orchestration: Docker, Kubernetes (K8s) for scalable microservice deployment.
- Automated Testing & Quality Gates: Integration of test automation and code quality tools (SonarQube, static analysis).
- Cloud Deployment & Management: AWS, Azure, Google Cloud Platform (GCP) automation.
- Monitoring & Logging: Prometheus, Grafana, ELK Stack, Datadog, CloudWatch for traceability and observability.
- 2. MLOps & AI Model Operations
- Model Lifecycle Management: Automated workflows from model training to deployment and retraining.
- Experiment Tracking & Version Control: MLflow, TFX, Kubeflow for experiment and artifact tracking.
- CI/CD for Models: Integrated pipelines that support data, model, and code changes across environments.
- Model Registry & Rollback: Versioned model deployments with rollback support for reliability.
- Monitoring & Drift Detection: Real-time monitoring of model performance and data drift.
- 3. Cloud & Hybrid Infrastructure Automation
- Cloud Architecture Design: Scalable, secure cloud environments with auto-scaling and cost optimization.
- Hybrid & Multi-Cloud Deployments: Cross-platform orchestration and automation.
- Security & Compliance Automation: Secure pipelines with automated compliance checks and access control.
- 4. Observability & Incident Response
- Real-Time Metrics & Dashboards: Custom observability dashboards with alerts and anomaly detection.
- Incident Playbooks & Automation: Automated remediation workflows (self-healing scripts, runbooks).
- 5. Collaboration & Workflow Optimization
- GitOps & Best Practices: Git-centric workflows for reproducible deployments.
- Workflow Consulting & Training: Best-practice transformation for engineering teams.
Tools & Technologies
- DevOps Tools
- CI/CD: Jenkins, GitHub Actions, GitLab CI/CD, CircleCI, ArgoCD
- Cloud & Automation: AWS (CodePipeline, CloudFormation), Azure DevOps, GCP Cloud Build
- IaC: Terraform, Pulumi, Ansible
- Containers: Docker, Kubernetes, Helm
- Monitoring: Prometheus, Grafana, Datadog, New Relic, ELK Stack
- Security & Compliance: Vault, AWS IAM, Azure RBAC
- MLOps Tools
- ML Lifecycle: MLflow, Kubeflow, TFX, SageMaker Pipelines
- Experiment Tracking: MLflow, Weights & Biases (W&B)
- Model Registry & Serving: TensorFlow Serving, TorchServe, KFServing
- Workflow Orchestration: Apache Airflow, Prefect
- Monitoring & Drift: WhyLabs, Evidently, Prometheus
- Cloud ML Services: AWS SageMaker, Azure ML, GCP Vertex AI
Typical Business Use Cases
- Software Delivery & Digital Products
- Rapid deployment of web and mobile applications
- Zero-downtime rollouts and rollback capabilities
- Automated test & release cycles
- AI/ML Enablement
- Scalable model deployment and inferencing pipelines
- Real-time model performance monitoring
- Automated retraining workflows
- Enterprise Cloud Transformation
- Migrate legacy systems to cloud-native architectures
- Containerization and microservices adoption
- Cost-optimized infrastructure management
- Data-Driven Continuous Improvement
- Integrate analytics into engineering workflows
- Continuous feedback loops from production telemetry
Call to Action
Optimize deployments, reduce cycle times, and maintain high reliability across software and machine learning systems. Contact AiPearlz for a DevOps & MLOps strategy session and tailored implementation roadmap.
Frequently Asked Questions
01QUESTION-01
What are DevOps & MLOps services?
DevOps services automate software delivery pipelines, infrastructure, and operations, while MLOps extends these practices to the machine learning lifecycle — from model training and deployment to monitoring and governance.
02QUESTION-02
What tools are used in DevOps and MLOps?
Common DevOps tools include Jenkins, GitHub Actions, Docker, Kubernetes, Terraform, and monitoring stacks like Prometheus/Grafana. MLOps uses MLflow, Kubeflow, Airflow, model registries, and cloud ML services for end-to-end model operations.
03QUESTION-03
How long does a DevOps & MLOps project take?
Smaller pipeline integrations may take 3–6 weeks; full platform implementations with automated workflows, cloud setup, and model lifecycle integration may take 8–14+ weeks, depending on complexity.
04QUESTION-04
Can you integrate ML model workflows into existing DevOps setups?
Yes. MLOps pipelines are designed to integrate into existing DevOps workflows to ensure consistent, automated delivery for both software and models.