Pular para o conteúdo principal
InicioCódigo juntoMachine Learning

Managing Machine Learning Models with MLflow

Learn to use MLflow to track and package a machine learning model, and see the process for getting models into production.
mar. de 2024
Code along with us onCode Along

Machine Learning applications are complex and can be difficult to track, hard to reproduce, and problematic to deploy. MLflow is designed to simplify the challenges of managing the machine learning lifecycle.

In this code-along you'll learn to use MLflow to track and package a machine learning model, and see the process for getting models into production. Throughout the code-along, you’ll learn how to get started with MLflow, how to make a reproducible machine learning model, and how to get started with model tracking and packaging.

Key Takeaways:

  • Learn how to get started with MLflow.
  • Learn the steps needed to make a reproducible model.
  • Learn about model tracking and packaging.

Resources

Temas
Relacionado

tutorial

Streamline Your Machine Learning Workflow with MLFlow

Take a deep dive into what MLflow is and how you can leverage this open-source platform for tracking and deploying your machine learning experiments.
Moez Ali 's photo

Moez Ali

12 min

tutorial

Turning Machine Learning Models into APIs in Python

Learn to how to create a simple API from a machine learning model in Python using Flask.
Sayak Paul's photo

Sayak Paul

20 min

tutorial

An End-to-End ML Model Monitoring Workflow with NannyML in Python

Learn an end-to-end workflow to monitor any model in your Jupyter notebook in production environments.
Bex Tuychiev's photo

Bex Tuychiev

15 min

tutorial

Machine Learning, Pipelines, Deployment and MLOps Tutorial

Learn basic MLOps and end-to-end development and deployment of ML pipelines.
Moez Ali's photo

Moez Ali

19 min

código junto

Running Machine Learning Experiments in Python

In this webinar, you'll use MLflow to manage a machine learning experiment pipeline. The session will cover model evaluation, hyperparameter tuning, and MLOps strategies, using a London weather dataset.
Folkert Stijnman's photo

Folkert Stijnman

código junto

Getting Started with Machine Learning in Python

Learn the fundamentals of supervised learning by using scikit-learn.
George Boorman's photo

George Boorman

See MoreSee More