Kyryl Truskovskyi – Machine Learning in Production is a complete end-to-end MLOps training program designed to help you move beyond building models and start deploying, scaling, and maintaining machine learning systems in real-world environments.
This course focuses on one critical transformation:
going from model-focused data scientist to full-stack ML engineer capable of production deployment
Instead of an isolated theory, you’ll learn how to handle the entire ML lifecycle—from infrastructure setup to monitoring deployed models—using modern tools and production-ready workflows.
Machine Learning in Production – What’s Included
Inside this program, you get a complete MLOps system:
- 164 structured lessons
- 14+ hours of video training
- 8-week step-by-step roadmap
- Hands-on projects and practice exercises
- Capstone end-to-end ML project
- Reusable code templates and design documents
Course Curriculum (Full Breakdown)
Week 1: Infrastructure & DevOps for ML
- Docker and containerization
- Kubernetes orchestration
- CI/CD pipelines for ML systems
- Cost management and deployment setup
Week 2: Data Engineering & RAG Systems
- Data storage and processing pipelines
- Data versioning and validation
- Labeling workflows
- Retrieval-Augmented Generation (RAG) fundamentals
Week 3: Experimentation & Optimization
- Structuring ML projects
- Experiment tracking and management
- Performance optimization techniques
Week 4: Workflow Orchestration
- Dagster, Kubeflow, and Airflow
- Automating ML pipelines
- Managing complex workflows
Week 5–6: Model Deployment & Scaling
- Serving ML and LLM models
- Scaling production systems
- Deployment strategies for real applications
Week 7: Monitoring & Maintenance
- Model monitoring and observability
- Detecting data drift
- Maintaining long-term performance
Week 8: Platform Integration & Tools
- AWS SageMaker
- Google Vertex AI
- Vendor selection strategies
- Modern ML infrastructure trends
Capstone Project
- Build a full end-to-end ML system
- Apply all learned concepts
- Create a production-ready portfolio project
What Makes This Course Different
Most ML courses stop at modeling.
This one focuses on production and real-world deployment.
Key advantages:
- Covers the full ML lifecycle
- Hands-on, project-based learning
- Focus on modern MLOps tools
- Includes LLM and RAG systems
- Built for real-world applications
Key Benefits
- Learn how to deploy ML models
- Build scalable ML systems
- Master MLOps workflows
- Work with modern AI infrastructure
- Create production-ready projects
Who This Course Is For
- Data scientists moving to production
- ML engineers and AI developers
- Software engineers entering ML
- AI professionals working with LLMs
- Anyone serious about MLOps
About the Instructor
Kyryl Truskovskyi specializes in production-level machine learning systems and teaching engineers how to deploy and scale AI solutions in real environments.
Machine Learning in Production by Kyryl Truskovskyi
Kyryl Truskovskyi – Machine Learning in Production is a complete system for mastering MLOps and building real-world machine learning applications.
If you want to go beyond notebooks and learn how to deploy, scale, and maintain ML systems in production, this course provides a clear and practical roadmap.
Get Kyryl Truskovskyi – Machine Learning in Production.
Sales Page: https://edu.kyrylai.com/courses/ml-in-production
