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.

Understand key tools and concepts for MLOps workflows.
Learn to deploy, monitor, and secure ML models.
Build robust AI pipelines using Docker and Kubernetes.
Identify and mitigate model drift and bias.

Syllabus

Courses under this program:
Course 1: Learning Docker
-Learn how to use Docker to deploy and manage applications as images that run on containers—a simpler approach than virtual machines or configuration management tools.

Course 2: Learning Kubernetes
-Learn how to set up a cluster, deploy applications, and manage those applications with Kubernetes.

Course 3: Generative AI and Predictive AI in the Cloud: Foundational Concepts and Scenarios
-Discover this easy-to-understand course for beginners that teaches the fundamentals of generative AI and predictive AI, as well as how they can relate to cloud computing.

Course 4: MLOps Tools: MLflow and Hugging Face
-Master MLflow and Hugging Face, two powerful open-source platforms for MLOps

Course 5: Data Preparation, Feature Engineering, and Augmentation for AI Models
-Explore the advanced data engineering techniques used to build generative AI systems.

Course 6: MLOps and Data Pipeline Orchestration for AI Systems
-Discover the essential skills and core concepts of MLOps and data pipeline orchestration for AI systems.

Course 7: MLOps Essentials: Model Development and Integration
-Get started with MLOps Concepts for Model Development and Integration, to organize machine learning (ML) development and deliver scalable and reliable ML products.

Course 8: MLOps Essentials: Model Deployment and Monitoring
-Learn how to deploy and monitor machine learning models to deliver scalable, reliable ML products and services.

Course 9: MLOps Essentials: Monitoring Model Drift and Bias
-Learn about the growing field of MLOps and the modeling techniques used to monitor model drift and bias.

Course 10: Introduction to MLSecOps
-Learn how to build security into your machine learning and AI lifecycles with MLSecOps.