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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! 🚀

(4.8) 13 reviews

145 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 learn!

  • Model Version Management

  • Model Pipeline Optimization

  • ML Workflow Optimization

  • Model Experiment Tracking

Master MLflow: From Tracking Machine Learning Experiments to Deploying Models!

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

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

  • Track and manage experiments using MLflow


  • Automated model version management and deployment

  • How to efficiently run machine learning workflows

  • Improve reproducibility and productivity of ML projects

From model experimentation to deployment, all in one place! Transform your machine learning workflow with MLflow. 🔥

💡 Lecture Planning Background

Applying machine learning to real-world services requires tracking model experiments, comparing performance, and automating deployment . However, many developers and data scientists often struggle to keep track of their experiments in Excel or manage model versions, leading to confusion .

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

Get started today to automate your ML experiments and maximize your AI project productivity ! 🚀

🏛 Learn these things

Core components of MLflow

Master MLflow's core features to easily implement experiment tracking, model management, and automated deployment . Learn how to increase the efficiency and reproducibility of your ML projects through hands-on learning!

MLflow Key Features

Machine Learning Life Cycle

Understand the entire lifecycle of a machine learning model, from development to deployment, and learn how to leverage MLflow at each stage to track experiments, manage models, and automate operations.

Machine Learning Life Cycle

It's good to listen together 🧑🏻‍🏫

331680

Data Science Fundamentals with Silicon Valley Engineers

Data science is hot these days. Learn and apply it in your own way! 💡
Gain hands-on experience from data analysis to algorithm implementation with essential tools like Anaconda, Numpy, Pandas, and Scikit-learn!
Learn how to gain insights and solve problems with data in a fun and easy way . 🎯

🤔 Things to note before taking the class

Practice environment

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


  • PC specifications

    • CPU: 4 cores or more

    • RAM: 8GB

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

    • Docker: Docker Desktop or Docker Engine

Learning Materials

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

Player Knowledge and Precautions

  • This lecture's practical environment is set up with Docker. If you'd like to learn more about Docker, I recommend checking out my free Docker course . Lecture link: [ https://inf.run/8eFCL ]

  • If you have any questions during the class, please feel free to leave them. However, since I'm located in the western United States, 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

10,805

Learners

746

Reviews

309

Answers

4.8

Rating

25

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

13 reviews

4.8

13 reviews

  • 정예찬님의 프로필 이미지
    정예찬

    Reviews 5

    Average Rating 5.0

    5

    62% enrolled

    • 미쿡엔지니어
      Instructor

      안녕하세요 정예찬님, 시간내서 좋은 리뷰 남겨주셔서 감사합니다!

  • 김진학님의 프로필 이미지
    김진학

    Reviews 1

    Average Rating 5.0

    5

    100% enrolled

    • 안녕하세요 김진학님, 시간내서 좋은 평가해주셔서 감사합니다.

  • pdy님의 프로필 이미지
    pdy

    Reviews 20

    Average Rating 5.0

    5

    33% enrolled

    • 안녕하세요 pdy님, 시간내서 좋은 점수 주셔서 감사합니다.

  • genie097님의 프로필 이미지
    genie097

    Reviews 1

    Average Rating 5.0

    5

    33% enrolled

    • 안녕하세요 genie097님, 시간내서 좋은 리뷰 주셔서 감사합니다.

  • 한규리님의 프로필 이미지
    한규리

    Reviews 5

    Average Rating 5.0

    5

    33% enrolled

    • 안녕하세요 한규리님, 시간내서 좋은 평가해주셔서 감사합니다.

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