Machine Learning Engineer Practice

95% of machine learning engineer practice is ____!

Andrew Ng, one of the four leading AI experts, recently explained the importance of MLOps at an online conference. He argued that we must move beyond model-centric thinking and focus on MLOps and data. The engineers who can accomplish this are machine learning engineers.

But did you know that writing model code only accounts for 5% of the overall machine learning project effort?
In reality, 95% of the work is spent on building data pipelines, preprocessing data, and serving models.

Machine Learning Courses: Practical, by, and for Practical Use

_{This is what it means to be a machine learning engineer!}

Machine learning engineers build machine learning pipelines, automate the work of machine learning projects, and dramatically increase the productivity of research organizations.

There are plenty of machine learning courses available, but few offer practical, hands-on AI production courses.
So, after taking the lecture, you can become an engineer who can solve the problems given in the project.
We have created a lecture that selects only the content that is absolutely necessary for practical use.

Through this course, you will acquire the machine learning engineering skills necessary for practical work.
I hope you can complete your project successfully.

This is what you can do after taking this course! 📖

• You can automatically generate machine learning experiment reports.
  Go to the project dashboard of a real knowledge sharer (click the link!)
  

  

  _{Example of a Real-World Machine Learning Experiment Report}
• AutoML - Find optimal parameters through hyperparameter tuning.
• You can build machine learning pipelines with Kubeflow.
  

  

  _{Example of building a real-world machine learning pipeline}
• You can manage your models through mlflow.
• You can learn how to serve models with bentoml.
  
  

People like this will love hearing this! 🔑

• Software engineers looking to transition to a machine learning career
• AI researchers who want to manage machine learning experiments well and create attractive reports.
• Developers who need to work on machine learning projects but are at a loss due to lack of relevant knowledge

Introducing myself ✒️

personal history

Current) Riiid VP of AIOps
Current) Google Developer Expert for ML
Former Naver AI Research Engineer
Former Kakao Data Engineer

Portfolio/Personal Videos

Frequently Asked Questions 💬

Q. Will machine learning pipelines help my career?
A. I can say with certainty. It's the most important technology in the AI industry right now. I've consulted with countless companies, and I've found that most of them have a thirst for this very machine learning pipeline. If you visit the technology introduction pages of AI companies, you'll always find MLOps-related technologies. They explain how to efficiently collect and train data.

Q. Can I listen even if I don't know much about development?
A. The recommended course is for those with some development knowledge, but it is structured so that you can basically follow along without thinking.

Q. What level of coverage do you cover?
A. We will cover the basic concepts of machine learning pipelines and the code quality management, experiment management, model management, and serving API construction required in practice.