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CODE: NAI12
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course focuses on the principles and practices of MLOps (Machine Learning Operations), with a strong emphasis on Continuous Integration and Continuous Deployment (CI/CD) for ML models. Participants will learn how to automate the ML lifecycle from data ingestion and model training to testing, deployment, and monitoring using tools like Git, Docker, Kubernetes, MLflow, and cloud services. The course blends theory and hands-on labs to help learners streamline model delivery, ensure reproducibility, and scale ML workflows efficiently.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand the MLOps lifecycle and its importance in production ML.
Build reproducible ML pipelines with version control and automation.
Use CI/CD tools to test, validate, and deploy machine learning models.
Containerize ML workflows using Docker and orchestrate with Kubernetes.
Monitor model performance and detect drift in real-time.
Apply security, governance, and compliance best practices in MLOps.
Leverage cloud-based MLOps platforms like Azure ML, SageMaker, or Vertex AI.
Collaborate across teams using robust CI/CD workflows for ML.
At the end of this course, you’ll understand:
This course is tailored for machine learning engineers, DevOps engineers, data scientists, software developers, and IT professionals responsible for deploying and maintaining ML systems. It is also important for technical project leads and architects seeking to implement scalable, reliable, and automated ML pipelines.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
