Machine Learning Engineer Practice

Name: Machine Learning Engineer Practice
Price: 61600 KRW
Rating: 4.4 (87 reviews)

This course will help you reduce the amount of trial and error you may experience in machine learning projects. I am in charge of Luid's machine learning pipeline and will teach you from the basics.

(4.4) 87 reviews

897 learners

Chris Song

Machine Learning(ML)

Reviews from Early Learners

What you will gain after the course

Machine Learning Experiment Management
Hyperparameter optimization
Automating the creation of machine learning experiment reports
Data Verification TFDV
Model Analysis
Research Code Quality Control
Kubeflow hands-on
Model repository mlflow practice
Model serving bentoML 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.

Recommended for
these people

Who is this course right for?

People who want to apply machine learning in practice
Anyone who wants to reduce technical debt in their machine learning projects

Need to know before starting?

Python
Machine Learning/Deep Learning Basics

Hello
This is

1,055

Learners

Reviews

Answers

4.4

Rating

Courses

(현) 뤼이드 VP of AIOps

(현) Google Developer Expert for Machine Learning

(전) Naver - AI Research Engineer

(전) Kakao - Data Engineer

Curriculum

All

16 lectures ∙ (10hr 15min)

Section 1. Introduction to Machine Learning Pipeline

2 lectures ∙ (1hr 29min)

Section 2. Machine Learning Project Experiment Management

2 lectures ∙ (1hr 16min)

Section 3. Code quality, Data validation, Model analysis

3 lectures ∙ (2hr 8min)

5. Research Code Quality Management
59:26
6. Data Validation - Tensorflow Data Validation
37:10
7. Machine Learning Model Analysis What if tool
31:24

Section 4. Docker & Kubernetes Basics

2 lectures ∙ (1hr 1min)

Section 5. Kubeflow Machine Learning Pipeline

5 lectures ∙ (2hr 22min)

Section 6. Model Management and Serving

2 lectures ∙ (1hr 57min)

Published:

Last updated:

Reviews

All

87 reviews

4.4

87 reviews

haenarashin
Reviews 9
∙
Average Rating 4.4
4
100% enrolled
Good points 1. You can learn practical approaches to fairly deep and realistic topics in a short period of time. 2. You can see useful tools such as wandb, wit, and implementation processes such as docker, kubernetes, kubeflow, mlflow, and bentoML. 3. You can obtain practical/practical approaches or know-how. Bad points 1. It is not a lecture just for 'Inflearn lectures', but it seems to have been recorded and uploaded as is from other talks? or lectures? => There are many distracting parts, and the lecture recording quality is a bit disappointing compared to other lectures for the same price. I purchased and listened to quite a few lectures from Inflearn, and watched lectures on other platforms, but if you only look at the lecture quality, it would be difficult to get a good evaluation. 2. Continuing from 1, the unrefined explanations/personal opinions/lecture materials are disappointing. It seems like something that could happen because it wasn't filmed just for 'Inflearn'. (I think Inflearn doesn't do any pre-review of the lecture content at all.) 3. Since you have to leave a course review to receive the lecture materials, it's difficult to follow along while watching the video. Of course, it might be different if you write a course review before completing the course, but I personally don't want to leave a useless review, so I plan to leave a review after completing the course and watch the lecture materials while listening to it repeatedly. Overall As it was announced that the lecture target is 'intermediate or higher', I don't recommend it if you don't have some experience or have heard of it. However, it's a good opportunity to quickly look through it while you have experience and see the approach from a practitioner's perspective. Also, if you're someone who wants to study machine learning/deep learning models diligently and acquire MLOps skills, it's not bad to listen to it once. If you take this course and also complete the MLOps course that recently opened on Coursera, it seems like a good combination.
czangyeob
Reviews 2
∙
Average Rating 4.5
4
100% enrolled
There aren't many Korean materials on MLOps, so I appreciate you for giving a good lecture so that others can easily access it. However, if you had paid more attention to the editing part, it would have been much better (I can't help but feel like you just uploaded a recorded lecture from somewhere else...). Overall, it seems like a good lecture to take a quick look at.
p14001238587
Reviews 3
∙
Average Rating 4.7
4
100% enrolled
It was a good lecture, but the process of setting up the environment was an old version, so it took a long time to solve the problem. I think the lecture needs to be updated or maintained. Of course, the lecture itself is really good. Thank you.
etfpro
Reviews 2
∙
Average Rating 3.0
4
81% enrolled
In the ML field, the most time-consuming and costly thing is not model development, but data preparation (preprocessing, transformation, etc.) and deploying and continuously maintaining the model for operation after learning the ML model. The tool that supports this is MLOps, and this course is very helpful for practical work in a situation where there is not much information about MLOps.
ramanuzan
Reviews 1
∙
Average Rating 4.0
4
100% enrolled
It's good that I got to know many useful tools while taking the lecture. However, it's a bit disappointing that the lecture is not targeted at Infraon, but is copied from another seminar.

Limited time deal

$48.40

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Machine Learning Engineer Practice

Reviews from Early Learners

What you will gain after the course

95% of machine learning engineer practice is ____!

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

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

People like this will love hearing this! 🔑

Introducing myself ✒️

personal history

Portfolio/Personal Videos

Frequently Asked Questions 💬

Recommended for these people

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Curriculum

Reviews

Chris Song's other courses

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