END TO END MLOPS ENGINEERING
Challenge
Businesses often struggle to deploy, manage, and monitor machine learning models in production reliably.
- Manual deployment processes are error-prone, non-reproducible, and difficult to scale.
- Ensuring version control, environment management, and continuous monitoring across development, staging, and production is challenging.
Approach
- Automate machine learning workflows, including model training, evaluation, and deployment, following MLOps best practices.
- Integrate data pipelines, model orchestration, and infrastructure provisioning to support the end-to-end ML lifecycle.
- Implement continuous integration and continuous delivery (CI/CD) pipelines to enable reproducible deployments.
Data
- Orchestrated Data Pipeline with Structured and unstructured data from multiple sources, including databases, CSV, and JSON files.
- Feature engineering and preprocessing steps tracked to ensure model consistency and reproducibility.
- Dataset splits and derived features managed through DVC and pipeline orchestration
Solution
- Automated CI/CD pipelines using Azure DevOps for testing, training, and deployment workflows.
- Data versioning and pipeline orchestration managed with DVC and Airflow to ensure reproducibility.
- Experiment and model tracking using MLflow for auditability and rollback capabilities.
- Deployment endpoints via Flask or FastAPI, with containerization on Azure Container Registry (ACR) and scalable hosting on AKS.
- Monitoring dashboards using Evidently AI to track data drift, input/output distributions, and model performance.
- Blob storage for raw, transformed, and processed data with logging for all pipeline activities.
- Infrastructure as code via Pulumi to provision and manage Azure VMs, AKS clusters, and other resources.
- Separate development, staging, and production environments to ensure safe, reliable deployments.
Business Impact
- Reliable and scalable ML model deployment, reducing manual intervention.
- End-to-end reproducibility, improving operational efficiency and reducing errors.
- Continuous monitoring and version control, ensuring model performance over time.
- Scalable infrastructure, capable of handling increasing workloads seamlessly.
- Enhanced data-driven decision-making through robust, automated, and auditable ML operations.