MLOps & Production Systems
Bridge the gap between model development and production deployment with comprehensive MLOps training. Master containerization, orchestration, and monitoring systems.
Course Overview
This comprehensive MLOps training program equips engineers with the skills needed to build robust machine learning pipelines that operate reliably in production environments. You'll learn to implement continuous integration and deployment for ML models while establishing effective monitoring systems for model performance tracking.
Core Technologies
Master containerization with Docker, orchestration with Kubernetes, and model serving strategies using various frameworks. Gain hands-on experience with modern MLOps tools and platforms.
Production Focus
Learn experiment tracking, model versioning, and A/B testing methodologies. Address real-world challenges including model drift, feature stores, and ensuring reproducibility across environments.
Career Impact & Outcomes
Enhanced Employability
MLOps engineers are in high demand across Tokyo's tech sector. Our graduates typically see a 40-60% salary increase within six months of course completion.
Production Readiness
Graduate with a portfolio of production-grade ML systems that demonstrate your ability to handle enterprise-level deployment challenges.
Industry Connections
Connect with hiring managers from leading tech companies during our networking sessions and project showcase events.
Recent Graduate Achievements
Akiko Tanaka
Promoted to Senior ML Engineer at Rakuten within 4 months of graduation
James Rodriguez
Led deployment of ML pipeline serving 2M+ daily requests at SoftBank
Professional Tools & Technologies
Docker & Containerization
Master container-based deployment strategies, multi-stage builds, and optimization techniques for ML workloads.
Kubernetes Orchestration
Deploy and manage ML services at scale using Kubernetes, including auto-scaling and resource management.
CI/CD Pipelines
Implement automated testing, model validation, and deployment workflows using Jenkins, GitLab CI, and GitHub Actions.
Monitoring & Observability
Set up comprehensive monitoring using Prometheus, Grafana, and specialized ML monitoring tools like Evidently AI.
Feature Store Management
Design and implement feature stores using Feast and cloud-native solutions for consistent feature serving.
Cloud Platforms
Deploy across AWS, GCP, and Azure using managed ML services like SageMaker, Vertex AI, and Azure ML.
Safety Protocols & Standards
Security Best Practices
Secure model serving with authentication and encryption protocols
Secrets management and environment variable security
Container security scanning and vulnerability assessment
Role-based access control and audit logging
Quality Assurance
Automated testing frameworks for ML models and pipelines
Model versioning and rollback strategies
Bias detection and fairness validation protocols
Compliance with data protection regulations
Who Should Enroll
Data Scientists
Transform your models from notebooks to production systems. Learn to bridge the gap between experimentation and deployment.
Software Engineers
Extend your DevOps expertise into machine learning operations. Master the unique challenges of ML system deployment.
ML Engineers
Advance your MLOps knowledge with modern tools and industry best practices for scalable production systems.
Prerequisites
Technical Requirements
Recommended Background
Progress Measurement & Tracking
Assessment Methods
Hands-on Projects
Build and deploy complete MLOps pipelines for real-world scenarios
Technical Presentations
Present your solutions to industry professionals and receive feedback
Code Reviews
Participate in peer reviews following industry best practices
Success Metrics
Portfolio Deliverables
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Ready to Master MLOps?
Join our comprehensive MLOps training program and transform your career in machine learning engineering.
3 Chome-2-1 Kasumigaseki, Chiyoda City, Tokyo 100-0013, Japan
+81 3-3597-8330