Master the engineering practices behind production ML systems in this intensive 5-day, hands-on training with a dedicated lab environment.
The MLOps Engineer Training Course is a rigorous, hands-on program for practitioners who build, deploy, and maintain machine learning systems at scale. Over five days, you will work through real-world engineering challenges including automating ML pipelines with CI/CD, serving models in production, designing feature stores, and orchestrating containerized ML workloads.
Each module pairs theory with extensive lab exercises in a dedicated cloud-based environment. You will build and deploy working ML pipelines, configure model serving endpoints, and implement automated testing and monitoring. By course end, you will be ready for the Certified MLOps Engineer exam.
Engineering skills to build and operate ML systems at production scale.
Design and implement continuous integration and delivery pipelines tailored for ML workflows, including automated testing, validation gates, and model registry integration.
Deploy models using REST and gRPC endpoints, implement batched and real-time inference, and optimize latency and throughput for production workloads.
Build and manage feature stores for consistent feature serving across training and inference, and design reusable feature transformation pipelines.
Containerize ML workloads with Docker, orchestrate training and serving jobs with Kubernetes, and manage resource allocation for GPU-intensive tasks.
Construct robust data ingestion, transformation, and validation pipelines that feed ML models reliably with tools like Apache Airflow and similar orchestrators.
Synthesize everything in a hands-on capstone where you design, build, deploy, and monitor a complete ML system from data pipeline to production endpoint.
Built for practitioners who want to engineer production ML systems.
Machine learning engineers who want to strengthen their skills in CI/CD, model serving, and infrastructure automation for ML workloads.
DevOps professionals transitioning into MLOps who need to learn the specific tooling and patterns required by machine learning systems.
Data engineers expanding their scope to cover ML pipeline construction, feature engineering at scale, and model deployment infrastructure.
Platform and infrastructure engineers tasked with building internal ML platforms and supporting data science teams with scalable tooling.
5 Days / 40 Hours
8 hours per day
Live Virtual via Zoom
or In-Person Classroom
Cloud-based lab access
included for all participants
Monday to Friday
9:00 AM - 5:00 PM
Six intensive modules blending engineering theory with hands-on lab practice.
This course directly prepares you for the Certified MLOps Engineer examination. Every module maps to specific exam domains, and the hands-on labs reinforce the practical knowledge tested on the exam. Your enrollment includes a certification exam voucher.
The Certified MLOps Engineer credential demonstrates your ability to design, build, and operate ML infrastructure at production scale. It is recognized across the industry as a mark of hands-on engineering competence.
View Certification DetailsEverything you need to master MLOps engineering in one comprehensive package.
Per participant
Group discounts available for 5+ participants. Contact us for corporate pricing.
Enroll today and build the skills that industry leaders demand.