MLflow with Silicon Valley Engineers

Are you still managing ML experiments manually? 🤔 Automate everything from experiment tracking to model deployment with MLflow, and dramatically boost your development productivity! An essential skill for data scientists and ML engineers 💡 Start right now! 🚀

(4.7) 21 reviews

225 learners

Level Beginner

Course period Unlimited

mlflow
mlflow
Machine Learning(ML)
Machine Learning(ML)
mlops
mlops
Deep Learning(DL)
Deep Learning(DL)
AI
AI
mlflow
mlflow
Machine Learning(ML)
Machine Learning(ML)
mlops
mlops
Deep Learning(DL)
Deep Learning(DL)
AI
AI

Reviews from Early Learners

4.7

5.0

성현

62% enrolled

I'm leaving a review after listening to about the halfway point. It was impressive how you kindly and clearly explained the important parts of using MLflow. I'll listen to the rest of the lecture carefully and apply it to my work. Thank you for providing such a good lecture at a reasonable price.

5.0

jin.kang

100% enrolled

Thank you for the great lecture.

5.0

Jongtae Ham

62% enrolled

The balance between concept explanation and practice is appropriate. It is very helpful.

What you will gain after the course

  • Model Version Management

  • Model Pipeline Optimization

  • ML Workflow Optimization

  • Model Experiment Tracking

Complete Guide to MLflow: From Machine Learning Experiment Tracking to Model Deployment!

MLflow is used by various companies including Uber, Databricks, Microsoft, and is an essential tool for data scientists and ML engineers to efficiently develop and deploy models.

#mlflow, #machine learning, #mlops, #deep learning, #artificial intelligence(AI)

  • Experiment Tracking and Management with MLflow


  • Model Version Management and Deployment Automation

  • ✅ How to efficiently operate machine learning workflows

  • Improving Reproducibility and Productivity in ML Projects

From model experimentation to deployment in one go! Revolutionize your machine learning workflow with MLflow. 🔥

💡Course Planning Background

To apply machine learning to actual services, experiment tracking, performance comparison, and deployment automation are essential. However, many developers and data scientists experience confusion by recording experiments in Excel or not managing model versions.

This course teaches you how to systematically manage experiments using MLflow and build an efficient model deployment process through MLOps.

Start right now and automate ML experiments, maximize AI project productivity! 🚀

🏛 Here's what you'll learn

MLflow's Core Components

Learn the core features of MLflow to easily implement experiment tracking, model management, and deployment automation. Discover how to enhance the efficiency and reproducibility of your ML projects through hands-on practice!

MLflow Key features

Machine Learning Life Cycle

Understand the entire lifecycle from machine learning model development to deployment, and learn how to use MLflow at each stage to perform experiment tracking, model management, and operational automation.

Machine Learning Life Cycle

Great to take together 🧑🏻‍🏫

331680

Data Science Fundamentals with a Silicon Valley Engineer

Learn hot data science these days in your own way! 💡
Experience everything from data analysis to algorithm implementation with essential tools like Anaconda, Numpy, Pandas, and Scikit-learn!
Learn how to gain insights from data and solve problems in an easy and fun way. 🎯

🤔 Things to Note Before Enrolling

Practice Environment

  • Operating System and Version (OS): macOS, Linux, Windows + Docker


  • PC Specifications

    • CPU: 4 cores or more

    • RAM: 8GB

    • Storage: 20GB or more free space (for Docker images & data storage)

    • Docker: Docker Desktop or Docker Engine

Learning Materials

  • I'll provide PDF lecture materials (refer to each video learning material) and code materials.

Prerequisites and Important Notes

  • The practical exercises in this course are set up using Docker. If you'd like to learn more about Docker, I recommend checking out my free Docker course. Course link: [https://inf.run/z6G4E]

  • If you have any questions during the course, please feel free to leave them. However, since I'm on the US West Coast, it may take some time for me to respond.

Recommended for
these people

Who is this course right for?

  • Data Scientist who wants to systematically manage ML experiments

  • ML Engineer who needs model deployment and management automation

  • A developer who wants to build highly reproducible ML workflows.

  • AI/ML practitioners wanting to apply MLflow in practice

  • Person aiming for efficient data-driven decisions.

Need to know before starting?

  • Python – Basic Syntax and Library Usage

  • Machine Learning – Model Training and Evaluation Concepts

  • Pandas & NumPy – Data Processing and Analysis

Hello
This is altoformula

23,238

Learners

1,226

Reviews

351

Answers

4.8

Rating

30

Courses

Are you going to finish in Korea? Penetrate the global market with English! 🌍🚀

Hello. I majored in Computer Science (EECS) at UC Berkeley 💻, have worked as a software engineer in Silicon Valley for over 15 years, and am currently a Staff Software Engineer working with Big Data and DevOps at a Big Tech headquarters in Silicon Valley.

  • 🧭 I would now like to share the technologies and know-how I learned firsthand at the forefront of innovation in Silicon Valley with all of you through online lectures.

  • 🚀 Join me, having learned and grown at the forefront of technological innovation, and develop the skills to compete on the global stage!

  • 🫡 I may not be the smartest, but I want to emphasize that you can achieve anything if you stay consistent and never give up. I will always be by your side, supporting you with great resources.

 

More

Curriculum

All

21 lectures ∙ (2hr 44min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

21 reviews

4.7

21 reviews

  • boobykong님의 프로필 이미지
    boobykong

    Reviews 1

    Average Rating 5.0

    5

    62% enrolled

    The balance between concept explanation and practice is appropriate. It is very helpful.

    • altoformula
      Instructor

      Hello Jongtae Ham, Thank you so much for taking the time to leave a great review! I'm really glad it was helpful!

  • codetomorrow님의 프로필 이미지
    codetomorrow

    Reviews 14

    Average Rating 5.0

    5

    62% enrolled

    I'm leaving a review after listening to about the halfway point. It was impressive how you kindly and clearly explained the important parts of using MLflow. I'll listen to the rest of the lecture carefully and apply it to my work. Thank you for providing such a good lecture at a reasonable price.

    • altoformula
      Instructor

      Hello Seonghyeon, Thank you for taking the time to leave a good review. It's rewarding to see my hard work pay off. I'm proud that the lecture was helpful to someone.

  • xinql님의 프로필 이미지
    xinql

    Reviews 19

    Average Rating 4.9

    5

    33% enrolled

    I only vaguely knew about MLflow, but after taking this lecture, I got a sense of how it can be used in practice. The structure is neat and the examples are well-organized, making it easy to follow along. Highly recommended for those interested in MLOps!

    • altoformula
      Instructor

      Hello qiulong Xin, Thank you for taking the time to leave us a great review!

  • hyojk2001님의 프로필 이미지
    hyojk2001

    Reviews 3

    Average Rating 5.0

    5

    100% enrolled

    This was a great help in building a platform related to MLOps. Thanks to this lecture, I was able to study to use it in various fields such as ML/DL.

    • altoformula
      Instructor

      Hello XL galasy, Thank you for taking the time to leave such a great review!

  • jinkang5537님의 프로필 이미지
    jinkang5537

    Reviews 5

    Average Rating 4.8

    5

    100% enrolled

    Thank you for the great lecture.

    • altoformula
      Instructor

      Hello jin.kang, Thank you for taking the precious time to leave such a great review.

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