MLOps & Production Machine Learning Systems
Learn to deploy, monitor, and maintain machine learning systems at scale in production environments.
Back to HomeAbout This Course
This 12-week practical program focuses on production challenges and solutions for machine learning systems. Master containerization with Docker, orchestration with Kubernetes, and ML pipelines with Kubeflow while learning to deploy models at scale.
Cover essential topics including model versioning, A/B testing, continuous integration for ML, data versioning, experiment tracking, and model registry management. Develop skills in monitoring model performance, detecting drift, and implementing automated retraining workflows.
Work with major cloud platforms including AWS SageMaker, Google Vertex AI, and Azure ML. Learn to handle critical issues like data privacy, model fairness, and regulatory compliance in production ML systems.
Duration
12 weeks of practical training focused on production ML system deployment and operations
Technologies
Docker, Kubernetes, Kubeflow, MLflow, AWS, Google Cloud, Azure, Terraform
Investment
€2,350 EUR - Complete training materials and cloud platform access included
Career Impact & Professional Growth
MLOps expertise is increasingly valuable as organizations move from experimental ML projects to production systems. This course prepares professionals to bridge the gap between model development and operational deployment.
Production Deployment
Develop capability to take models from development to production, handling scaling, reliability, and performance requirements.
System Reliability
Learn to build robust ML systems with monitoring, alerting, and automated response to performance degradation and drift.
Cross-Team Collaboration
Enable effective collaboration between data scientists, engineers, and operations teams through standardized MLOps practices.
MLOps Tools & Infrastructure
The program covers essential tools and platforms for building production-grade machine learning systems with focus on practical implementation.
Containerization & Orchestration
- Docker containerization for reproducible ML environments
- Kubernetes orchestration for scalable model deployment
- Kubeflow pipelines for end-to-end ML workflows
ML Pipeline Management
- MLflow for experiment tracking and model registry
- Data versioning with DVC and feature stores
- CI/CD pipelines for automated model deployment
Cloud Platforms
- AWS SageMaker for end-to-end ML workflows
- Google Vertex AI for unified ML platform services
- Azure Machine Learning for enterprise deployments
Monitoring & Observability
- Prometheus and Grafana for metrics monitoring
- Model drift detection and alerting systems
- Performance tracking and automated retraining triggers
Production Standards & Best Practices
The course emphasizes industry best practices for building reliable, maintainable, and scalable machine learning systems in production environments.
Deployment Strategies
A/B Testing
Implement controlled experiments to validate model improvements before full deployment, measuring business impact.
Canary Deployments
Gradually roll out model updates to subsets of users, monitoring performance before wider release.
Rollback Procedures
Establish automated rollback mechanisms for quick recovery from problematic deployments or performance issues.
System Reliability
Automated Retraining
Design systems that detect performance degradation and trigger retraining workflows automatically.
Drift Detection
Implement monitoring for data drift and concept drift that can degrade model performance over time.
Health Monitoring
Continuous monitoring of system health metrics, latency, throughput, and model prediction quality.
Who This Course Is For
This program is designed for ML practitioners ready to build production-grade systems and engineers transitioning into MLOps roles.
ML Practitioners
Data scientists and ML engineers who develop models and want to learn how to deploy and maintain them in production environments.
DevOps Engineers
Infrastructure and operations professionals looking to specialize in ML system deployment and operations with modern tools.
Technical Leads
Engineering managers and technical leads responsible for ML infrastructure and seeking to understand MLOps architecture and practices.
Performance Tracking & System Validation
The course covers comprehensive approaches to monitoring, measuring, and validating ML system performance in production environments.
System Metrics
Monitor critical infrastructure and application metrics including latency, throughput, resource utilization, and error rates to ensure system reliability.
- Response time and latency tracking
- Resource consumption monitoring
- Service availability and uptime
Model Performance
Track model-specific metrics to detect performance degradation, drift, and quality issues that require investigation or retraining.
- Prediction quality and accuracy tracking
- Data distribution monitoring
- Concept drift detection
Business Impact Measurement
Connect technical metrics to business outcomes through dashboards and reporting systems. Learn to measure the actual impact of ML systems on key business metrics and communicate this value to stakeholders effectively.
Governance & Compliance
Understanding regulatory requirements and ethical considerations is essential for production ML systems. The course covers key topics in ML governance.
Data Privacy
Implement practices for handling sensitive data, understanding GDPR requirements, and building privacy-preserving ML systems.
Model Fairness
Learn to detect and address bias in ML systems, implementing fairness metrics and mitigation strategies in production.
Regulatory Compliance
Understand documentation requirements, audit trails, and compliance standards relevant to ML systems in regulated industries.
Ready to Master Production ML Systems?
Connect with us to learn how this practical program can help you build reliable, scalable machine learning systems in production.