회원 가입일: 2019
Transformer 모델 및 BERT 모델
Earned 9월 18, 2023 EDT
Serverless Design with AWS Lambda
Earned 11월 30, 2020 EST
Advanced Operations Using Amazon Redshift
Earned 11월 23, 2020 EST
Big Data on AWS
Earned 11월 21, 2020 EST
BigQuery ML을 사용한 예측 모델링을 위한 데이터 엔지니어링
Earned 10월 29, 2020 EDT
[DEPRECATED] Data Engineering
Earned 2월 6, 2020 EST
머신러닝용 BigQuery
Earned 1월 30, 2020 EST
이 과정은 Transformer 아키텍처와 BERT(Bidirectional Encoder Representations from Transformers) 모델을 소개합니다. 셀프 어텐션 메커니즘 같은 Transformer 아키텍처의 주요 구성요소와 이 아키텍처가 BERT 모델 빌드에 사용되는 방식에 관해 알아봅니다. 또한 텍스트 분류, 질문 답변, 자연어 추론과 같이 BERT를 활용할 수 있는 다양한 작업에 대해서도 알아봅니다. 이 과정은 완료하는 데 대략 45분이 소요됩니다.
In this Quest, you will learn how to write functions with the AWS Lambda Service that respond to events and integrate other AWS Services. You will create applications that write records to Amazon DynamoDB, send messages with Amazon SNS, and monitor events in Amazon CloudWatch and external services. You will even write a back-end function in Lambda for creating a voice-response app for Alexa and the Amazon Echo.
In this Quest, you will delve deeper into the uses and capabilities of Amazon Redshift. You will use a remote SQL client to create and configure tables, and gain practice loading large data sets into Redshift. You will explore the effects of schema variations and compression. You will explore visualization of Redshift data, and connect Redshift with Amazon Machine Learning to create a predictive data model.
Scientists, developers, and other technologists from many different industries are taking advantage of AWS to perform big data analytics and meet the challenges of the increasing volume, variety, and velocity of digital information. AWS offers a portfolio of cloud computing services to help you manage big data by reducing costs, scaling to meet demand, and increasing the speed of innovation. In this quest, you’ll learn to work with advanced services for Big Data.
중급 BigQuery ML을 사용한 예측 모델링을 위한 데이터 엔지니어링 기술 배지를 획득하여 Dataprep by Trifact로 데이터 변환 파이프라인을 BigQuery에 빌드, Cloud Storage, Dataflow, BigQuery를 사용한 ETL(추출, 변환, 로드) 워크플로 빌드, BigQuery ML을 사용하여 머신러닝 모델을 빌드하는 기술 역량을 입증할 수 있습니다.
This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
SQL만으로 몇 시간이 아닌 몇 분 만에 머신러닝 모델을 빌드하고 싶으신가요? BigQuery ML은 데이터 분석가가 기존 SQL 도구와 기술을 사용하여 머신러닝 모델을 만들고, 학습시키고, 평가하고, 예측할 수 있게 하여 머신러닝을 범용화합니다. 이 실습 시리즈에서는 다양한 모델 유형을 실험하고 좋은 모델을 만드는 요소를 알아봅니다.