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Training and certification can be arranged in partnership with Boğaziçi University Lifelong Learning Center if requested.
Program curriculum is informational. Trainings can be customized for organizational needs. For more info, Contact Us
This corporate course is also available for individuals.
This advanced MLOps training thoroughly covers strategies and practices for comprehensively managing the entire lifecycle of machine learning models, from deployment to real-time monitoring, automatic updates, scalability, security, and performance optimization. This training aims to equip participants with the advanced concepts and techniques necessary for designing and managing complex MLOps infrastructures at the enterprise level.
Advanced MLOps goes beyond basic MLOps processes and covers complex topics such as model drift monitoring, A/B testing, canary deployment, advanced CI/CD pipelines, automatic model retraining, real-time data stream management, and security policies. This training reveals strategic approaches to optimizing the continuous integration, deployment, and monitoring of large-scale and critical machine learning projects.
This training is suitable for the following individuals:
Experienced data scientists, machine learning engineers, and DevOps specialists
Systems engineers managing enterprise-level MLOps infrastructure
IT managers seeking to optimize the performance and security of models in production
Professionals wanting to learn advanced model management, monitoring, and automation processes
All technology leaders working on large-scale machine learning projects
Strategic Model Management: Optimizes complex model lifecycles, automated updates, and model drift monitoring.
Real-Time Monitoring and Updates: Continuously monitors the performance of models in production, providing automated interventions when necessary.
Scalability and Security: Strengthens infrastructure design, security, and compliance standards to meet large data flows and high traffic requirements.
Advanced Automation: Accelerates production processes with A/B testing, canary deployment, rollback strategies, and automated retraining processes.
Enterprise Application: Learns advanced MLOps approaches implemented by industry leaders to increase project sustainability and efficiency.
Detailed model development · training · deployment · continuous improvement cycle
Model drift · performance measurement · quality control strategies
Simultaneous testing of different model versions
Managing gradual update processes with canary deployment strategies
Automatic retraining mechanisms upon detecting performance decline
Feedback loop and model update policies
Real-time data processing techniques · data stream management
Apache Kafka · Spark Streaming integration
Creating scalable environments using Docker · Kubernetes
Serverless architectures · microservice integration
Load balancing strategies · distributed storage solutions
Ensuring high availability · fault tolerance
Automated model integration and deployment in CI/CD processes
Use of Jenkins · GitLab CI · MLflow · Kubeflow
Model validation · performance testing · automated rollback mechanisms
Improving continuous integration processes with test automation
Pipeline monitoring · log analysis · error detection
Alert systems · automated intervention strategies
Model performance · latency · resource usage metrics
Anomaly detection · model drift · performance degradation analysis
Prometheus · Grafana integration
Log analysis · alert mechanisms · visual dashboard designs
Optimization strategies to improve model performance
Scalability · Efficient management of resource usage
Data encryption · access control · authentication mechanisms
Measures against adversarial attacks and model manipulation
Data privacy and compliance requirements such as GDPR · HIPAA
Ethical use · fair modeling · bias management strategies
Response plans for security breaches · data loss · system failures
Continuous improvement · risk mitigation strategies
Case studies from large-scale enterprise MLOps projects
Success factors · challenges · solution strategies
Advanced MLOps pipeline setup and simulations
Real-time problem-solving sessions with group work
Examples from participant projects · sharing of solution proposals
Q&A sessions · advanced discussions
