Introduction to MLOps with MLFlow
Introduction to MLOps with MLFlow, Mastering MLOps with MLflow: Bridging the Gap Between Data Science and Deployment.
Machine Learning Operations, or MLOps, is a crucial discipline that bridges the gap between data science and IT operations to streamline the deployment and management of machine learning models. In this introductory course on MLOps, participants will gain a comprehensive understanding of the fundamental concepts, tools, and best practices necessary to operationalize machine learning models effectively.
- Understand the Importance of MLOps
- What is MLFlow & Its components?
- Components of MLFlow – Deep Dive
- Features of MLFlow
- Getting Started with MLFlow
- Implementing the MLFlow
By the end of this course, participants will have a strong foundation in MLOps, enabling them to effectively implement and manage machine learning models in production environments. Whether you are a data scientist, machine learning engineer, or IT professional, this course will empower you to optimize the machine learning lifecycle and contribute to the success of your organization’s data-driven initiatives.
Join us on this course, and explore and learn the importance of MLOPs and implement it from scratch in a hands on manner using MLFlow Library
MLflow is an open-source platform designed to manage the end-to-end machine learning (ML) lifecycle. It was developed by Databricks, a company that specializes in big data analytics and machine learning solutions, and is now maintained by a broader community of contributors. MLflow provides tools and components to streamline and standardize the ML development and deployment process. It consists of several key components and functionalities:
- Tracking: MLflow allows you to log and track experiments, parameters, metrics, and artifacts associated with your machine learning projects. You can easily compare different runs and keep a record of model performance over time.
- Projects: With the Projects component, you can package your machine learning code, including dependencies, into a reproducible format. This makes it easier to share and collaborate on ML projects across different environments.
- Models: MLflow provides a model registry to organize, version, and manage trained machine learning models. You can easily deploy and serve these models in a production environment.
- Registry: The Model Registry offers a centralized hub for storing and managing machine learning models. It supports model versioning, tracking of model lineage, and collaboration among team members.
- REST API: MLflow provides a REST API that allows you to interact with and control various aspects of the platform programmatically, making it easy to integrate with other tools and systems.
- UI Interface: MLflow includes a web-based user interface for visualizing experiments, comparing models, and managing the model registry. This interface makes it user-friendly and accessible to a wide range of users.