Advanced hands-on bootcamp of MLOps with MLFlow, Scikit-learn, CI/CD, Azure, FastAPI, Gradio, SHAP, Docker, DVC, Flask..

If you're looking for a comprehensive, hands-on, and project-based guide to learning MLOps (Machine Learning Operations), you've come to the right place.

According to an Algorithmia survey, 85% of Machine Learning projects do not reach production. In addition, the MLOps have exponentially grown in the last years. MLOPS was estimated at $23.2 billion for 2019 and is projected to reach $126 billion by 2025. Therefore, MLOps knowledge will give you numerous professional opportunities.

This course is designed to teach everything related to MLOps, from model development, model registration, and model versioning; model performance monitoring, CI/CD, cloud deployment, model serving and APIs, and web applications development to punt into production the model.

We will guide you through the MLOps skills, sharing clear explanations and valuable professional advice.

With visual training, downloadable study guides, hands-on exercises, and real-world labs, this is the only course you'll need to learn how to implement an end-to-end MLOps project. By the end of this course, not only will you have developed an entire MLOps project from the ground up, but you will also gain the knowledge and confidence to apply these same concepts to your projects.

What does the course include?

MLOps fundamentals. We will learn about the Basic Concepts and Fundamentals of MLOps. We will look at traditional ML model management challenges and how MLOps addresses those problems to offer solutions.

MLOps toolbox. We will learn how to apply MLOps tools to implement an end-to-end project.

Model versioning with MLFlow. We will learn to version and register machine learning models with MLFlow. MLflow is an open source platform for managing the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.

Auto-ML and Low-code MLOps. We will learn to automate the development of machine learning models with Auto-Ml and Low-code libraries such as Pycaret. Pycaret automates much of the MLOps cycle, including model versioning, training, evaluation, and deployment.

Explainability, Auditability, and Interpretable machine learning. Learn about model interpretability, explainability, auditability, and data drift with SHAP and Evidently.

Containerized Machine Learning WorkFlow With Docker. Docker is one of the most used tools to package the code and dependencies of our application and distribute it efficiently. We will learn how to use Docker to package our Machine Learning applications.

Deploying ML in Production through APIS. We will learn about deploying models to production through API development with FastAPI and Flask. We will also learn to deploy those APIs in the Azure Cloud using Azure containers.