Mastering MLOps: Complete Course on ML Operations Course Overview

To ensure that you can fully benefit from the Mastering MLOps: Complete Course on ML Operations, we recommend that you have the following minimum prerequisites:

• Basic understanding of machine learning concepts and workflows
• Familiarity with Python programming, as it is commonly used in machine learning projects
• Experience with Git version control for managing and sharing code
• Some exposure to data handling and preprocessing techniques
• Knowledge of machine learning frameworks such as scikit-learn or TensorFlow
• An understanding of basic command-line interface (CLI) operations

Please note that while these prerequisites are intended to set you up for success, we have designed our course materials to be as inclusive as possible. Our instructors are committed to helping all students, regardless of their starting skill level, to grasp the concepts and practices of MLOps.

Target Audience for Mastering MLOps: Complete Course on ML Operations

Mastering MLOps: Complete Course on ML Operations is designed for professionals seeking to streamline ML workflows and enhance model deployment and management.

• Machine Learning Engineers
• Data Scientists
• DevOps Engineers
• AI/ML Researchers
• IT Professionals with a background in data and machine learning
• Software Developers interested in MLOps
• Technical Project Managers overseeing ML projects
• Data Engineers looking to specialize in machine learning pipelines
• ML Product Managers
• Cloud Engineers focusing on ML deployment
• Technical Leads and Architects designing ML systems
• Operations Analysts involved in ML processes
• Professionals aiming for a career transition into MLOps
• Academics and students in computer science with a focus on machine learning

Learning Objectives - What you will Learn in this Mastering MLOps: Complete Course on ML Operations?

Introduction to Course Learning Outcomes

Gain mastery over MLOps with comprehensive training on tools, practices, and automation for efficient ML model management, deployment, and continuous integration and delivery.

Learning Objectives and Outcomes

• Understand the significance of MLOps and how it solves challenges faced in traditional ML model management.
• Apply MLOps tools and practices to manage end-to-end ML projects, ensuring efficient versioning, collaboration, and deployment.
• Master the use of MLFlow for model versioning, lifecycle management, and integration with other MLOps tools.
• Learn data versioning techniques with DVC, including integration with different storage options.
• Create and manage a shared ML repository using tools like DagsHub, DVC, Git, and MLFlow for collaborative model development.
• Automate the ML model development process using Auto-ML tools like Pycaret and streamline model lifecycle phases.
• Gain insights into model interpretability, explainability, and auditability, with hands-on experience in SHAP and Evidently.
• Package and distribute machine learning applications efficiently using Docker for a containerized ML workflow.
• Deploy ML models through API development with FastAPI and Flask, and understand the deployment process on Azure Cloud.
• Develop web applications with embedded ML models using Gradio, and deploy production-ready apps using Docker.
• Integrate BentoML with MLFlow for automated ML service development and learn deployment strategies using Docker.
• Implement Continuous Integration and Delivery (CI/CD) in MLOps with GitHub Actions and Continuous Machine Learning (CML).
• Monitor and evaluate machine learning models in production using tools like Evidently AI and Deepchecks for quality assurance.
• Execute a capstone MLOps project that encompasses model development, versioning, API building, web app creation, and CI/CD implementation.