OVERVIEW
Program Overview
Bridge the gap between data engineering and AI deployment. This program equips you with the practical skills to build, deploy, and manage machine learning models at scale. You’ll master MLOps tools, cloud-based ML platforms, and automation techniques to ensure models perform reliably in real-world production environments.
CERTIFICATE
Certificate: ML Engineering Associate/Professional
A practical ML engineering program focused on building, deploying, and managing high-performance machine learning models.
TOOLS
Key Tools
We leverage industry-leading technologies and platforms to build reliable, scalable, and data-driven solutions that empower innovation and growth.
CONTENTS
Curriculum
Understand the complete end-to-end process of developing, deploying, and maintaining machine learning models in production.
Learn to create, transform, and select the most impactful features to improve model performance and accuracy.
Master techniques for training, tuning, and validating models to ensure robust, reliable predictions.
Implement best practices for managing ML workflows — including version control, automated testing, and continuous integration.
Deploy models efficiently using APIs and scale them for real-time or batch inference across production environments.
Track model performance post-deployment, detect data drift, and automate retraining pipelines.
Package and manage ML applications using Docker containers and Kubernetes clusters for scalability and reliability.
Leverage cloud platforms like AWS SageMaker, Google AI Platform, and Azure Machine Learning to operationalize machine learning workflows.
Start your data journey today.
Join thousands of professionals advancing their careers in ML Engineering
