GCP Data Platform Hands-On
Free
GCP 자격에 부끄럽지 않은 실무 능력을 갖추기 위하여, LAB 중심의 실무 강좌를 다음과 같이 준비 하였습니다.
먼저 LAB 메뉴얼을 업데이트 하겠습니다. (동영상 강좌 추후 추가 예정)
| No. | 내용 | 완료일 |
| 1 | Explore a BigQuery Public Dataset | 2019-09-19 |
| 2 | Recommend Products using ML with Cloud SQL and Dataproc | 2019-09-19 |
| 3 | Predict Visitor Purchases with a Classification Model with BigQuery ML | 2019-09-19 |
| 4 | Create a Streaming Data Pipeline for a Real-Time Dashboard with Cloud Dataflow | 2019-09-19 |
| 5 | Classify Images with Pre-built ML Models using Cloud Vision API and AutoML | 2019-09-20 |
| 6 | Create a Dataproc Cluster | 2019-09-20 |
| 7 | Work with structured and semi-structured data | 2019-09-20 |
| 8 | Submit Dataproc jobs for unstructured data | 2019-09-20 |
| 9 | Leveraging Unstructured Data | 2019-09-20 |
| 10 | Cluster automation using CLI | 2019-09-20 |
| 11 | Add Machine Learning | 2019-09-20 |
| 12 | Building a BigQuery Query | 2019-09-23 |
| 13 | Loading and Exporting Data | 2019-09-23 |
| 14 | Advanced SQL Queries | 2019-09-23 |
| 15 | A Simple Dataflow Pipeline (Python) | 2019-09-23 |
| 16 | MapReduce in (Python) | 2019-09-23 |
| 17 | Side Inputs (Python) | 2019-09-23 |
| 18 | Explore dataset, create ML datasets, create benchmark | 2019-09-23 |
| 19 | Getting Started with TensorFlow | 2019-09-23 |
| 20 | Machine Learning using tf.estimator | 2019-09-23 |
| 21 | Refactoring to add batching and feature-creation | 2019-09-23 |
| 22 | Distributed training and monitoring | 2019-09-23 |
| 23 | Scaling up ML using Cloud ML Engine | 2019-09-23 |
| 24 | Feature Engineering | 2019-09-23 |
| 25 | Publish streaming data into Pub/Sub | 2019-09-23 |
| 26 | Streaming Data Pipelines | 2019-09-23 |
| 27 | Streaming Analytics and Dashboards | 2019-09-23 |
| 28 | Streaming data into Bigtable | 2019-09-23 |
Course Features
- Lectures 30
- Quizzes 17
- Duration 40 hours
- Skill level Level 200
- Language Korean
- Students 19
- Certificate No
- Assessments Self
-
사전준비
-
Contents
- Explore a BigQuery Public Dataset
- Check 01
- Recommend Products using ML with Cloud SQL and Dataproc
- Check 02
- Predict Visitor Purchases with a Classification Model with BigQuery ML
- Check 03
- Create a Streaming Data Pipeline for a Real-Time Dashboard with Cloud Dataflow
- Check 04
- Classify Images with Pre-built ML Models using Cloud Vision API and AutoML
- Check 05
- Create a Dataproc Cluster
- Check 06
- Work with structured and semi-structured data
- Check 07
- Submit Dataproc jobs for unstructured data
- Check 08
- Leveraging Unstructured Data
- Check 09
- Cluster automation using CLI
- Check 10
- Add Machine Learning
- Check 11
- Building a BigQuery Query
- Check 12
- Loading and Exporting Data
- Check 13
- Advanced SQL Queries
- Check 14
- A Simple Dataflow Pipeline (Python)
- Check 15
- MapReduce in (Python)
- Check 16
- Side Inputs (Python)
- Check 17
- Explore dataset, create ML datasets, create benchmark
- Getting Started with TensorFlow
- Machine Learning using tf.estimator
- Refactoring to add batching and feature-creation
- Distributed training and monitoring
- Scaling up ML using Cloud ML Engine
- Feature Engineering
- Publish streaming data into Pub/Sub
- Streaming Data Pipelines
- Streaming Analytics and Dashboards
- Streaming data into Bigtable
-
Survey
