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Machine Learning Operations (MLOps)
Getting Started - 한국어
Google Cloud Training
Google Cloud Training
이 과정에서는 Google Cloud에서 프로덕션 ML 시스템 배포, 평가, 모니터링, 운영을 위한 MLOps 도구와 권장사항을 소개합니다. MLOps는 프로덕션에서 ML 시스템을 배포, 테스트, 모니터링, 자동화하는 방법론입니다. 머신러닝 엔지니어링 전문가들은 배포된 모델의 지속적인 개선과 평가를 위해 도구를 사용합니다. 이들이 협력하거나 때론 그 역할을 하는 데이터 과학자는 고성능 모델을 빠르고 정밀하게 배포할 수 있도록 모델을 개발합니다.
Register for this course and see more details by visiting:
OpenCourser.com/course/l2hlj3/mlops
What's inside
Syllabus
머신러닝 작업(MLOps): 시작하기
이 모듈에서는 과정을 간략히 설명합니다
머신러닝 작업 도입
ML 실무자의 고충
ML의 DevOps 개념
세 가지 ML 수명 주기 단계
ML 프로세스 자동화
Read more
Syllabus
머신러닝 작업(MLOps): 시작하기
이 모듈에서는 과정을 간략히 설명합니다
머신러닝 작업 도입
ML 실무자의 고충
ML의 DevOps 개념
세 가지 ML 수명 주기 단계
ML 프로세스 자동화
Read more
Vertex AI 및 Vertex AI에서 MLOps
Vertex AI란 무엇이며 통합 플랫폼이 중요한 이유는 무엇인가요?
Vertex AI에서 MLOps 소개
Vertex AI는 MLOps 워크플로를 어떻게 지원하나요? 1부
Vertex AI는 MLOps 워크플로를 어떻게 지원하나요? 2부
요약
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
데이터 과학자와 머신러닝 엔지니어가 프로덕션에서 ML 시스템 배포, 평가, 모니터링 및 운영에 사용하는 MLOps 도구 및 모범 사례를 소개합니다
Google Cloud에서 제공하는 Vertex AI 및 Google Cloud Training에서 제공하는 교육자료를 활용합니다
머신러닝 엔지니어를 대상으로 설계되었습니다
머신러닝 엔지니어와 데이터 과학자가 ML 시스템을 효율적으로 배포하고 운영하는 데 도움이 될 수 있습니다
MLOps에 대한 전반적인 소개를 제공하며, 특정 산업 또는 분야에 초점을 맞추지 않습니다
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Machine Learning Operations (MLOps): Getting Started - 한국어.
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Career center
Learners who complete Machine Learning Operations (MLOps): Getting Started - 한국어 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They work in a variety of industries, including technology, finance, and healthcare. Machine Learning Engineers use a variety of tools and techniques to build models, including statistical modeling, data mining, and artificial intelligence. This course can help Machine Learning Engineers learn about the tools and techniques used to build and maintain machine learning models. It can also help them learn about the DevOps concepts used to automate the machine learning process.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. They use a variety of tools and techniques to automate the software development and deployment process. DevOps Engineers work in a variety of industries, including technology, finance, and healthcare. This course can help DevOps Engineers learn about the tools and techniques used to automate the software development and deployment process. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They work in a variety of industries, including technology, finance, and healthcare. Data Scientists use a variety of tools and techniques to analyze data, including statistical modeling, machine learning, and data visualization. This course can help Data Scientists learn about the tools and techniques used to analyze data and make predictions. It can also help them learn about the DevOps concepts used to automate the data analysis process.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work in a variety of industries, including technology, finance, and healthcare. Data Engineers use a variety of tools and techniques to build and maintain data pipelines, including data integration tools, data transformation tools, and data quality tools. This course can help Data Engineers learn about the tools and techniques used to build and maintain data pipelines. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Cloud Engineer
Cloud Engineer
Cloud Engineers design, build, and maintain cloud computing systems. They work in a variety of industries, including technology, finance, and healthcare. Cloud Engineers use a variety of tools and techniques to build and maintain cloud computing systems, including cloud computing platforms, cloud development tools, and cloud testing tools. This course can help Cloud Engineers learn about the tools and techniques used to build and maintain cloud computing systems. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. They work in a variety of industries, including technology, finance, and healthcare. Software Engineers use a variety of tools and techniques to build software systems, including programming languages, software development tools, and testing tools. This course can help Software Engineers learn about the tools and techniques used to build and maintain software systems. It can also help them learn about the DevOps concepts used to automate the software development process.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify and solve problems. They use a variety of tools and techniques to analyze business data, including statistical modeling, data visualization, and business process modeling. This course can help Business Analysts learn about the tools and techniques used to analyze business data and solve problems. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Project Manager
Project Manager
Project Managers work with teams to plan and execute projects. They use a variety of tools and techniques to manage projects, including project planning, project scheduling, and project budgeting. This course can help Project Managers learn about the tools and techniques used to plan and execute projects. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Product Manager
Product Manager
Product Managers work with businesses to develop and launch new products. They use a variety of tools and techniques to manage product development, including product roadmapping, product marketing, and product testing. This course can help Product Managers learn about the tools and techniques used to develop and launch new products. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Technical Writer
Technical Writer
Technical Writers create documentation for software and hardware products. They use a variety of tools and techniques to create documentation, including word processing software, technical writing tools, and graphics software. This course can help Technical Writers learn about the tools and techniques used to create documentation for software and hardware products. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts work with businesses to ensure that their software and hardware products meet quality standards. They use a variety of tools and techniques to test software and hardware products, including testing tools, test automation tools, and test management tools. This course can help Quality Assurance Analysts learn about the tools and techniques used to test software and hardware products. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Computer Scientist
Computer Scientist
Computer Scientists work with computers to solve problems. They use a variety of tools and techniques to solve problems, including programming languages, computer science theory, and algorithms. This course can help Computer Scientists learn about the tools and techniques used to solve problems. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Data Analyst
Data Analyst
Data Analysts work with data to identify trends and patterns. They use a variety of tools and techniques to analyze data, including statistical software, data mining tools, and data visualization tools. This course can help Data Analysts learn about the tools and techniques used to analyze data and identify trends and patterns. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Statistician
Statistician
Statisticians work with data to analyze trends and patterns. They use a variety of tools and techniques to analyze data, including statistical software, statistical modeling tools, and data visualization tools. This course can help Statisticians learn about the tools and techniques used to analyze data and identify trends and patterns. It can also help them learn about the MLOps concepts used to automate the machine learning process.
Systems Analyst
Systems Analyst
Systems Analysts work with businesses to analyze and improve their systems. They use a variety of tools and techniques to analyze and improve systems, including system analysis tools, system design tools, and system testing tools. This course can help Systems Analysts learn about the tools and techniques used to analyze and improve systems. It can also help them learn about the MLOps concepts used to automate the machine learning process.
For more career information including salaries, visit:
OpenCourser.com/course/l2hlj3/mlops
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning Operations (MLOps): Getting Started - 한국어.
머신러닝 엔지니어링의 이론적 기초와 실무적 적용에 대한 포괄적인 가이드입니다. 이 책에서는 모델 개발, 배포, 모니터링을 위한 최상의 사례와 도구를 다룹니다.
Save
DevOps 원칙과 최상의 사례에 대한 필수 가이드입니다. 이 책은 MLOps 프로세스에 통찰력을 제공하고 DevOps 원칙을 ML 생명 주기에 적용하는 방법을 보여줍니다.
머신러닝 모델을 프로덕션에 성공적으로 배포하는 방법에 대한 실용적인 가이드입니다. 이 책은 모델 평가, 모니터링, 유지 관리를 위한 최상의 사례와 도구를 다룹니다.
머신러닝과 딥러닝의 기초에 대한 포괄적인 소개입니다. 이 책은 Vertex AI에서 사용되는 PyTorch 프레임워크에 대한 실습적 지침을 제공합니다.
딥러닝의 기초에 대한 포괄적인 소개입니다. 이 책은 Vertex AI에서 사용되는 Keras 프레임워크에 대한 실습적 지침을 제공합니다.
파이썬을 사용한 머신러닝에 대한 포괄적인 가이드입니다. 이 책은 머신러닝의 기초와 Vertex AI에서 사용 가능한 다양한 라이브러리를 다룹니다.
통계적 학습의 이론적 기초에 대한 포괄적인 참고서입니다. 이 책은 머신러닝 모델의 개발과 평가를 이해하는 데 도움이 될 것입니다.
Save
데이터 마이닝과 머신러닝의 이론적 기초와 실습적 적용에 대한 포괄적인 가이드입니다. 이 책은 머신러닝 모델의 개발과 평가를 이해하는 데 도움이 될 것입니다.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l2hlj3/mlops
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Effort
3 weeks of study, 6-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
한국어
Good to know
데이터 과학자와 머신러닝 엔지니어가 프로덕션에서 ML 시스템 배포, 평가, 모니터링 및 운영에 사용하는 MLOps 도구 및 모범 사례를 소개합니다
Google Cloud에서 제공하는 Vertex AI 및 Google Cloud Training에서 제공하는 교육자료를 활용합니다
머신러닝 엔지니어를 대상으로 설계되었습니다
머신러닝 엔지니어와 데이터 과학자가 ML 시스템을 효율적으로 배포하고 운영하는 데 도움이 될 수 있습니다
MLOps에 대한 전반적인 소개를 제공하며, 특정 산업 또는 분야에 초점을 맞추지 않습니다
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