MLOps Essentials for Developers and AI Engineers: Tools, Pipelines, Security
Explore foundational tools for MLOps, including Docker, Kubernetes, MLflow, and Hugging Face, and understand how to prepare data and orchestrate AI pipelines. Courses cover the full model lifecycle—from development to deployment, monitoring, and bias detection—along with essential concepts in ML security. Ideal for developers and ML engineers, this path ensures learners are equipped to build scalable, secure, and production-ready AI systems.
Courses
- Learning Docker
- Learning Kubernetes
- Generative AI and Predictive AI in the Cloud: Foundational Concepts and Scenarios
- MLOps Tools: MLflow and Hugging Face
- Data Preparation, Feature Engineering, and Augmentation for AI Models
- MLOps and Data Pipeline Orchestration for AI Systems
- MLOps Essentials: Model Development and Integration
- MLOps Essentials: Model Deployment and Monitoring
- MLOps Essentials: Monitoring Model Drift and Bias
- Introduction to MLSecOps