Machine Learning
Machine Learning Engineering (MLOps)
Ship ML models to production
About this course
Bridge the gap between data science and software engineering with this advanced, project-based course. Learn to manage the entire machine learning lifecycle, from training to live deployment. You will master model packaging, automate deployment pipelines, and ensure your AI systems run flawlessly on modern cloud platforms. Gain the enterprise-grade skills required to build an unbreakable career shield in the tech industry with AI Expert Academy.
Curriculum
5 modules · click to expand
1 MLOps Essentials
Understand the core principles of Machine Learning Operations. Learn how MLOps bridges the gap between data scientists and IT operations to ensure smooth, scalable, and reliable AI system deployments.
2 Version Control for ML
Go beyond standard code tracking. Learn to use tools like Git and GitHub to version control your massive datasets, model parameters, and training experiments, ensuring complete reproducibility across your team.
3 Packaging ML Models
Containerize your machine learning algorithms to eliminate the "it works on my machine" problem. Master packaging tools to create portable, lightweight deployment environments that run consistently anywhere.
4 CI/CD for Models
Automate your entire machine learning pipeline. Build Continuous Integration and Continuous Deployment (CI/CD) workflows that automatically test, validate, and launch new model versions without manual intervention.
5 Monitoring in Production
Keep your AI performing at its peak. Learn to track model accuracy in real-time, detect data drift, and set up automated alerts to maintain the health and reliability of your live models.