Learning Introduction to MLFlow with Applications

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Free Download Introduction to MLFlow with Applications
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 51m | Size: 1.68 GB
Experiment Tracking, Model Registry, Reproducible ML Projects, and Production-Ready ML Workflows

What you'll learn
Track, compare, and analyze machine learning experiments using MLflow Tracking and the MLflow UI
Log parameters, metrics, artifacts, and models programmatically for reproducible ML experiments
Package and run machine learning projects using MLflow Projects with consistent environments
Register, version, and manage models using the MLflow Model Registry for production workflows
Requirements
Basic to intermediate Python programming experience
Prior knowledge of machine learning concepts (model training, evaluation metrics, overfitting, etc.)
Familiarity with at least one ML or data science library (e.g., scikit-learn, TensorFlow, PyTorch)
General understanding of software development concepts (scripts, environments, dependencies)
Description
Machine learning projects quickly become complex as experiments grow, models evolve, and teams scale. Tracking experiments manually, comparing results across runs, and managing model versions without proper tooling leads to lost time, irreproducible results, and fragile production systems.This course provides a practical, end-to-end guide to MLflow, the open-source standard for managing the machine learning lifecycle. You will learn how to track experiments, organize ML projects, register and version models, and deploy models in a structured and reproducible way.Rather than focusing on theory, this course is hands-on and workflow-oriented. You will work with real machine learning code and see how MLflow integrates into professional ML and software development pipelines. The course assumes you already understand machine learning concepts and Python-based ML libraries, and focuses on how to manage ML work properly in real-world environments.In this course, you will learn how to:Track experiments using MLflow Tracking and the MLflow UILog parameters, metrics, artifacts, and models programmaticallyCompare and query experiment results across multiple runsUse MLflow Projects to package ML code with reproducible environmentsManage model versions and lifecycle stages using the MLflow Model RegistryLoad, deploy, and serve models using MLflow ModelsDesign production-ready ML workflows that scale beyond notebooksThis course is not an introduction to machine learning. If you are looking to learn what models are or how training works, this is not the right course. Instead, this course is designed for practitioners who already build models and now want to professionalize their ML workflow.By the end of the course, you will be able to confidently use MLflow to manage real ML projects, collaborate with teams, and move models from experimentation to production in a clean and maintainable way.
Who this course is for
Machine Learning Engineers who want to manage experiments and models professionally
Data Scientists who run many experiments and need reproducibility and comparison tools
Software Engineers working on ML-powered systems and production ML pipelines
ML practitioners who want to move from ad-hoc experimentation to structured ML workflows
Homepage

https://www.udemy.com/course/introduction-to-mlflow-with-applications/

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