Video description
Introduction to MLflow for MLOpsLearn how to use MLflow for managing the machine learning lifecycle. Track experiments, package models, and deploy to production.
In this course you'll learn how to use MLflow - an open source platform for managing the machine learning lifecycle. You'll learn how to:
- Install MLflow and explore its components like the UI, tracking, and model packaging
- Log metrics, parameters, and artifacts to track ML experiments
- Create reproducible ML projects with MLflow for repeatable model training
- Package models and dependencies for deployment and serving
- Use model registries to version, stage, and deploy models
- Deploy models to tools like Azure ML and SageMaker
This course includes hands-on exercises, projects, and real-world examples so you can apply your new MLflow skills immediately.
Use the reference repository for MLFlow examples and projects:
Learning objectives
- Install and configure MLflow
- Use the tracking UI and APIs
- Log metrics, parameters, tags, and artifacts
- Create reproducible ML projects
- Version, stage, and deploy models with registries
- Deploy models to Azure ML, SageMaker, etc
Lesson 1: Introduction to MLflow
Lesson Outline
- Overview of MLflow components
- Installation and configuration
- Tracking experiments with UI, Python, R APIs
- Logging metrics, params, tags, artifacts
Lesson 2: MLflow Projects
Lesson Outline
- Motivation for reproducible ML projects
- Creating project directories
- Running projects locally or on Git
- Customizing execution environments
Lesson 3: MLflow Models
Lesson Outline
- Packaging models and dependencies
- Model versioning with registries
- Staging and promoting model stages
- Deploying models to services
About your instructor
Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University teaching Machine Learning, Cloud Computing, Data Engineering, Python, and Rust. With Alfredo's guidance, you will gain the knowledge and skills to work with MLFlow and apply it to MLOps tasks.
Resources
Table of contents
-
Lesson 1
- "Overview Of Mlflow"
- "Installing And Using Mlflow"
- "Introduction To The Tracking Ui"
- "Parameters Version Artifacts Metrics"
- "Working With Mlflow Projects"
- "Create An Mlflow Project"
- "Run Projects From Remote Git"
- "Connecting Mlflow To Databricks"
- "Components Of Mlflow Package"
- "Use A Registry With Mlflow"
- "Referencing Artifacts With The Api"
- "Serving Mlflow Models"
- Lesson 2
- Lesson 3
Product information
- Title: Introduction to MLflow for MLOps
- Author(s):
- Release date: August 2023
- Publisher(s): Pragmatic AI Solutions
- ISBN: 28188975VIDEOPAIML
You might also like
video
Fighting Churn Churn Forecasting: XGBoost ML forecasting and analysis
Make a predictive XGBoost model for churn and generate predictions for an out-of-sample dataset.
book
Kubernetes Application Developer: Develop Microservices and Design a Software Solution on the Cloud
Write efficient, smart, and optimized code for containerized applications on public and private clouds at a …
audiobook
What's New in AI: Open Source Large Language Models with Eric Xing (Audio)
Join host George Anadiotis and guest Eric Xing, for a discussion about the current and expanding …
video
Introduction to MLOps Walkthrough
A walkthrough of Chapter 1 of the O'Reilly book Practical MLOps. Topics include: * DevOps * …