What You'll Learn
Comprehensive curriculum designed by industry experts
Packaging ML Models
- Pickle/Joblib
- ONNX Format
- Creating APIs (FastAPI/Flask)
- Dockerizing Models
- Environment Management
- Dependencies
Orchestration & Serving
- Kubernetes Basics
- Kubeflow
- Serverless Inference
- A/B Testing Strategies
- Canary Deployments
- Scalability
ML Pipelines
- Reproducibility
- Experiment Tracking (MLflow)
- Data Versioning
- Automated Retraining
- Continuous Training (CT)
- Feature Stores
Monitoring & Maintenance
- Drift Detection (Data/Concept)
- Performance Monitoring
- Fairness & Bias Checks
- Debugging Production Models
- Cost Optimization
- Governance
Live Projects
Build real-world applications that look great on your portfolio
End-to-End MLOps Platform
Build a platform where users upload data and get a deployed API endpoint.
MLflowFastAPIDockerReact
Model Drift Monitor
Create a dashboard that alerts when model performance degrades.
PrometheusGrafanaPythonEvidently AI
Prerequisites
- Python
- Docker basics
- ML knowledge
Career Outcomes
- Deploy models to production
- Build CI/CD for ML
- Monitor AI capability
- Master MLOps tools
Tools You'll Master
DockerKubernetesMLflowFastAPIAWS
Certification
MLOps Engineer Certificate
Performance-based stipend
Apply for This Internship
Start your journey with AI Model Deployment (MLOps) Internship