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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) 18 reviews

176 learners

  • altoformula
3시간 만에 완강할 수 있는 강의 ⏰
실리콘밸리
mlflow
Machine Learning(ML)
mlops
Deep Learning(DL)
AI

Reviews from Early Learners

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

18,852

Learners

902

Reviews

332

Answers

4.8

Rating

28

Courses

한국에서 끝낼 거야? 영어로 세계 시장을 뚫어라! 🌍🚀

안녕하세요. UC Berkeley에서 💻 컴퓨터 공학(EECS)을 전공하고, 실리콘 밸리에서 15년 이상을 소프트웨어 엔지니어로 일해왔으며, 현재는 실리콘밸리 빅테크 본사에서 빅데이터와 DevOps를 다루는 Staff Software Engineer로 있습니다.

  • 🧭 실리콘 밸리의 혁신 현장에서 직접 배운 기술과 노하우를 온라인 강의를 통해 이제 여러분과 함께 나누고자 합니다.

  • 🚀 기술 혁신의 최전선에서 배우고 성장해 온 저와 함께, 여러분도 글로벌 무대에서 경쟁할 수 있는 역량을 키워보세요!

  • 🫡 똑똑하지는 않지만, 포기하지 않고 꾸준히 하면 뭐든지 이룰수 있다는 점을 꼭 말씀드리고 싶습니다. 항상 좋은 자료로 옆에서 도움을 드리겠습니다

 

Curriculum

All

21 lectures ∙ (2hr 44min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

18 reviews

4.7

18 reviews

  • mhlim2144님의 프로필 이미지
    mhlim2144

    Reviews 1

    Average Rating 5.0

    5

    100% enrolled

    • altoformula
      Instructor

      Hello mhlim, Thank you for taking the time to give a good score

  • abcd123123님의 프로필 이미지
    abcd123123

    Reviews 326

    Average Rating 5.0

    5

    19% enrolled

    • altoformula
      Instructor

      Hello Mr. Jeong Byeong-ju, You're really taking a lot of classes 😊😊😊. Thank you for taking the time to give us a good review.

  • srdn452928님의 프로필 이미지
    srdn452928

    Reviews 12

    Average Rating 5.0

    5

    62% enrolled

    • altoformula
      Instructor

      Hello Lee Eunryong, Thank you for taking the time to leave a good review.

  • slavefactory님의 프로필 이미지
    slavefactory

    Reviews 8

    Average Rating 5.0

    5

    62% enrolled

    • altoformula
      Instructor

      Hello Ye-chan Jeong, Thank you for taking the time to leave such a great review!

  • k04118725님의 프로필 이미지
    k04118725

    Reviews 1

    Average Rating 5.0

    5

    100% enrolled

    • altoformula
      Instructor

      Hello Mr. Kim Jin-hak, Thank you for taking the time to give us a good review.

$42.90

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