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👋 MLOps Course Introduction

Welcome to the MLOps and MLflow course! In this course, you will learn how to implement best practices of Machine Learning Operations (MLOps) using the open-source platform MLflow. MLOps is a set of practices and tools aimed at improving the collaboration and productivity of Machine Learning (ML) teams, as well as the reliability and reproducibility of ML models in production.

What will I learn?

  • Work with MLFlow: a tool used to record ML Experiments
  • Upload a trained model to MLFlow
  • Deploy a trained Model to an API

Through this course, you will learn how to use MLflow to manage the entire ML lifecycle, from data preparation and model training to deployment and monitoring. You will also learn how to create and manage MLflow projects, models, and registry, as well as how to deploy them as an API.

Whether you are a data scientist, machine learning engineer, or software developer, this course will provide you with the knowledge and skills to implement MLOps best practices and leverage the power of MLflow to streamline your ML workflow and improve the quality and reliability of your ML models.

What is MLOps?

MLOps is like a set of tools and practices that help data scientists and machine learning engineers manage and deploy their machine learning models effectively. It's a bit like having a well-organized kitchen with all the tools and processes in place to turn a great recipe into a delicious meal.

what_is_mlops

In the world of machine learning, MLOps helps streamline the entire process, from developing and testing models to deploying them in real-world applications. It ensures that models are reliable, easy to update, and can be used by others in the organization. Just as a well-organized kitchen makes cooking easier and more efficient, MLOps makes working with machine learning models more efficient and reliable.

MLOps simple analogy

MLOps is a bit like having a well-organized kitchen with all the tools and processes in place to turn a great recipe into a delicious meal.

🙋 Author

🙋 Name: Andrés Matesanz

📩 Email: Matesanz.Cuadrado@gmail.com

🌐 Website: https://matesanz.github.io/