Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Machine Learning in the Enterprise - 한국어
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.
-
Course by
-
Self Paced
-
الإنجليزية
Dataflow: Qwik Start - Templates
This is a self-paced lab that takes place in the Google Cloud console. This page shows you how to create a streaming pipeline using a Google-Provided Cloud Dataflow template.
-
Course by
-
Self Paced
-
1 ساعات
-
الإنجليزية
Reconciling Account Data with Cloud Spanner Change Streams
This is a self-paced lab that takes place in the Google Cloud console. Learn how to perform financial account reconciliation tasks using Cloud Spanner, Dataflow, and BigQuery.
-
Course by
-
Self Paced
-
1 ساعات
-
الإنجليزية
ETL Processing on Google Cloud Using Dataflow and BigQuery
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build several Data Pipelines that will ingest data from a publicly available dataset into BigQuery.
-
Course by
-
Self Paced
-
1 ساعات
-
الإنجليزية
Configuring MongoDB Atlas with BigQuery Dataflow Templates
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will provision a MongoDB Atlas cluster, create a dataflow pipeline to load data from the cluster to BigQuery
-
Course by
-
Self Paced
-
1 ساعات
-
الإنجليزية
Real Time Machine Learning with Cloud Dataflow and Vertex AI
This is a self-paced lab that takes place in the Google Cloud console. Implement a real-time, streaming machine learning pipeline that uses Cloud Dataflow and Vertex AI.…
-
Course by
-
Self Paced
-
الإنجليزية
Processing Data with Google Cloud Dataflow
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will simulate a real-time real world data set from a historical data set. This simulated data set will be processed from a set of tex…
-
Course by
-
Self Paced
-
الإنجليزية
Getting Started with Splunk Cloud GDI on Google Cloud
This is a self-paced lab that takes place in the Google Cloud console. A step-by-step guide through the process to configure multiple methods to ingest Google Cloud data into Splunk. In this hands-on lab you'll learn how to configure Google Cloud to send logging and other infrastructure data to Splunk Cloud via Dataflow, the Splunk Add-on for Google Cloud Platform, and Splunk Connect for Kubernetes (SC4K).
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Serverless Data Processing with Dataflow: Develop Pipelines
In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance.
-
Course by
-
Self Paced
-
19 ساعات
-
الإنجليزية
Dataflow: Qwik Start - Python
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will set up your Python development environment, get the Cloud Dataflow SDK for Python, and run an example pipeline using the Google Cloud Platform Console.
-
Course by
-
Self Paced
-
1 ساعات
-
الإنجليزية
Building Batch Data Pipelines on Google Cloud
Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
-
Course by
-
Self Paced
-
17 ساعات
-
الإنجليزية
Building Resilient Streaming Analytics Systems on Google Cloud
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using Google Cloud Skills Boost.
-
Course by
-
Self Paced
-
8 ساعات
-
الإنجليزية