Machine Learning in the Enterprise - 한국어 from Coursera

이 과정에서는 다양한 ML 비즈니스 요구사항과 사용 사례를 다루는 ML팀의 우수사례를 중심으로 ML 워크플로에 대한 실용적이고 현실적인 접근 방식을 포괄적으로 소개합니다. 이 팀은 데이터 관리 및 거버넌스에 필요한 도구를 이해하고, Dataflow 및 Dataprep에 대한 개괄적인 지식과 BigQuery를 사용한 사전 처리 작업 등을 바탕으로 데이터 사전 처리를 위한 가장 효과적인 접근 방식을 검토해야 합니다.

팀은 두 가지 구체적인 사용 사례에 맞는 머신러닝 모델을 빌드하는 세 가지 옵션을 제공합니다. 이 과정에서는 팀이 목표 달성을 위해 AutoML, BigQuery ML 또는 커스텀 학습을 사용해야 하는 이유를 설명합니다. 커스텀 학습에 대해서도 자세히 설명합니다. 학습 코드 구조, 스토리지, 대용량 데이터 세트 로드에서 학습된 모델 내보내기에 이르기까지 커스텀 학습 요구사항을 설명합니다.

Docker에 대한 지식이 거의 없어도 컨테이너 이미지를 빌드할 수 있는 커스텀 학습 머신러닝 모델을 빌드합니다.

우수사례팀에서 Vertex Vizier를 사용한 초매개변수 조정과 모델 성능을 개선하는 데 이를 어떻게 활용할 수 있는지 연구합니다. 모델 개선에 대한 이해를 높이기 위해 정규화와 희소성 처리, 그 외 많은 중요한 개념과 원칙 등 이론적인 내용도 자세히 살펴봅니다. 마지막으로 예측 및 모델 모니터링을 개략적으로 설명하고 Vertex AI를 사용하여 ML 모델을 관리하는 방법을 알아봅니다.

What's inside

Syllabus

Module 0 : 소개

이 모듈은 과정 및 과정 목표에 대한 개요를 제공합니다.

Module 1 : ML 엔터프라이즈 워크플로 이해하기: 모듈 소개 및 개요

이 모듈에서는 ML 엔터프라이즈 워크플로와 각 단계의 목적을 설명합니다.

기업에서의 데이터

이 모듈에서는 Feature Store, Data Catalog, Dataplex, Analytics Hub 등 Google의 엔터프라이즈 데이터 관리 및 데이터 거버넌스 도구를 살펴봅니다.

Module 3 : 머신러닝 및 커스텀 학습의 과학

이 모듈에서는 머신러닝 및 신경망의 기술과 과학을 살펴봅니다. Vertex AI를 사용하여 커스텀 ML 모델을 학습시키는 방법도 설명합니다.

Module 4 : Vertex Vizier 초매개변수 조정

이 모듈에서는 Vertex AI Vizier를 사용하여 초매개변수를 조정하는 방법을 설명합니다.

Module 5 : Vertex AI를 사용한 예측 및 모델 모니터링

이 모듈에서는 Vertex AI 예측 및 모델 모니터링에 대해 설명합니다. 먼저 사전 빌드 및 커스텀 컨테이너를 사용한 일괄 및 온라인 예측을 소개한 다음 ML 모델의 실적을 관리하는 서비스인 모델 모니터링을 살펴봅니다.

Module 6: Vertex AI Pipelines

이 모듈에서는 Vertex AI Pipelines와 ML 워크플로 조정을 위해 해당 파이프라인을 빌드하는 방법을 설명합니다.

Module 7 : ML 개발을 위한 권장사항

이 모듈에서는 Vertex AI의 여러 머신러닝 프로세스의 권장사항을 검토합니다.

Module 8 : 과정 요약

이 모듈은 기업에서의 머신러닝 과정의 요약입니다.

Module 9 : 시리즈 요약

이 모듈은 Google Cloud의 머신러닝 과정의 요약입니다.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

산업에서 표준인 빅데이터 분석과 기계 학습 워크플로를 탐구합니다

기계 학습 워크플로의 전반적인 개요와 팀워크에 대한 실질적인 접근 방식을 제공합니다

사전처리, 모델 구축을 위한 AutoML, BigQuery ML 및 커스텀 학습 옵션에 대한 정보를 제공합니다

컨테이너 이미지를 빌드하는 과정에서 Docker 지식이 거의 필요하지 않습니다

Vertex Vizier를 통한 초매개변수 조정과 모델 성능 향상 방법을 다룹니다

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning in the Enterprise - 한국어 with these activities:

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TensorFlow를 사용한 모델 훈련 연습

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TensorFlow의 기본 사항을 숙지하고 실습을 통해 모델 개발 기술을 향상시킵니다.

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TensorFlow 튜토리얼 및 문서 익히기
샘플 데이터 세트를 사용하여 시작하기
다양한 모델 유형 탐구 및 구현하기
모델 성능 평가 및 조정하기

Google Cloud 머신러닝 워크샵 참석

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Google Cloud 제품의 실습적 경험을 통해 머신러닝 기술을 향상시킵니다.

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Career center

Learners who complete Machine Learning in the Enterprise - 한국어 will develop knowledge and skills that may be useful to these careers:

Data Scientist

Vertex AI Pipelines is a platform for building and managing machine learning (ML) pipelines. As a Data Scientist, you will need to have a strong understanding of ML pipelines in order to build and deploy ML models. This course will teach you how to use Vertex AI Pipelines to build and manage ML pipelines. This will give you the skills you need to be successful in your role as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Vertex AI Pipelines is a platform for building and managing machine learning (ML) pipelines. As a Machine Learning Engineer, you will need to have a strong understanding of ML pipelines in order to build and deploy ML models. This course will teach you how to use Vertex AI Pipelines to build and manage ML pipelines. This will give you the skills you need to be successful in your role as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

This course provides a comprehensive overview of the machine learning (ML) workflow. As a Data Analyst, you will need to have a strong understanding of the ML workflow in order to prepare data for ML models. This course will teach you about the different stages of the ML workflow, including data collection, data preparation, and model evaluation. This will give you the skills you need to be successful in your role as a Data Analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

Machine learning (ML) is increasingly used to solve business problems. As a Business Analyst, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve business problems. This course will introduce you to the basics of ML and show you how to use ML to solve business problems. This will give you the skills you need to be successful in your role as a Business Analyst.

See salaries and explore the career path for Business Analyst

Software Engineer

Machine learning (ML) is increasingly used to solve software problems. As a Software Engineer, you will need to have a strong understanding of ML in order to build software that uses ML. This course will introduce you to the basics of ML and show you how to use ML to solve software problems. This will give you the skills you need to be successful in your role as a Software Engineer.

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Product Manager

Machine learning (ML) is increasingly used to build products. As a Product Manager, you will need to have a strong understanding of ML in order to make informed decisions about which ML features to build into your products. This course will introduce you to the basics of ML and show you how to use ML to build products. This will give you the skills you need to be successful in your role as a Product Manager.

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Marketing Manager

Machine learning (ML) is increasingly used to solve marketing problems. As a Marketing Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve marketing problems. This course will introduce you to the basics of ML and show you how to use ML to solve marketing problems. This will give you the skills you need to be successful in your role as a Marketing Manager.

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Sales Manager

Machine learning (ML) is increasingly used to solve sales problems. As a Sales Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve sales problems. This course will introduce you to the basics of ML and show you how to use ML to solve sales problems. This will give you the skills you need to be successful in your role as a Sales Manager.

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Financial Analyst

Machine learning (ML) is increasingly used to solve financial problems. As a Financial Analyst, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve financial problems. This course will introduce you to the basics of ML and show you how to use ML to solve financial problems. This will give you the skills you need to be successful in your role as a Financial Analyst.

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Operations Manager

Machine learning (ML) is increasingly used to solve operational problems. As an Operations Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve operational problems. This course will introduce you to the basics of ML and show you how to use ML to solve operational problems. This will give you the skills you need to be successful in your role as an Operations Manager.

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Human Resources Manager

Machine learning (ML) is increasingly used to solve human resources problems. As a Human Resources Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve human resources problems. This course will introduce you to the basics of ML and show you how to use ML to solve human resources problems. This will give you the skills you need to be successful in your role as a Human Resources Manager.

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Supply Chain Manager

Machine learning (ML) is increasingly used to solve supply chain problems. As a Supply Chain Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve supply chain problems. This course will introduce you to the basics of ML and show you how to use ML to solve supply chain problems. This will give you the skills you need to be successful in your role as a Supply Chain Manager.

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Customer Success Manager

Machine learning (ML) is increasingly used to solve customer success problems. As a Customer Success Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve customer success problems. This course will introduce you to the basics of ML and show you how to use ML to solve customer success problems. This will give you the skills you need to be successful in your role as a Customer Success Manager.

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Healthcare Manager

Machine learning (ML) is increasingly used to solve healthcare problems. As a Healthcare Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve healthcare problems. This course will introduce you to the basics of ML and show you how to use ML to solve healthcare problems. This will give you the skills you need to be successful in your role as a Healthcare Manager.

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Education Manager

Machine learning (ML) is increasingly used to solve education problems. As an Education Manager, you will need to have a strong understanding of ML in order to make informed decisions about how to use ML to solve education problems. This course will introduce you to the basics of ML and show you how to use ML to solve education problems. This will give you the skills you need to be successful in your role as an Education Manager.

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