SPSS Modeler 비주얼 인터페이스가 학습하기 쉬운 이유 중 하나는 각 노드에 명확하게 정의된 함수가 있기 때문입니다. 그러나 복잡한 처리의 경우 긴 노드 시퀀스가 필요할 수도 있습니다. 결국 이로 인해 플로우 캔버스가 복잡해지고 플로우 다이어그램을 따르기가 어려워질 수 있습니다.
길고 복잡한 플로우의 혼란을 방지하는 두 가지 방법이 있습니다.
처리 시퀀스를 여러 플로우로 분할할 수 있습니다. 예를 들어, 첫 번째 플로우는 두 번째 플로우가 입력으로 사용하는 데이터 파일을 작성합니다. 두 번째 스트림은
세 번째 스트림이 입력으로 사용하는 파일을 작성하며, 계속해서 이와 같이
반복됩니다. 그러나 다중 플로우를 관리해야 합니다.
복잡한 플로우 프로세스에 대해 작업할 때 보다 간소화된 대안으로 SuperNode 를 작성할 수 있습니다. SuperNodes 는 플로우의 섹션을 캡슐화하여 여러 노드를 단일 노드로 그룹화합니다. 이는 데이터 마이너에 이점을 제공합니다.
노드를 그룹화하면 네터 (neater) 및 더 관리하기 쉬운 플로우가 발생합니다.
노드를 하나의 비즈니스별 수퍼노드로 결합할 수 있습니다.
노드를 그룹으로 묶기 위해서는 SuperNode:
Ctrl 을 누른 상태에서 함께 그룹화하려는 노드를 클릭하십시오.
SuperNode작성을 클릭하십시오.
노드는 특수 별표 아이콘이 있는 단일 SuperNode 로 그룹화됩니다.그림 1. SuperNode 아이콘
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Cloud Pak for Data relationship map
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Tasks you'll do
Some tasks have a choice of tools and services.
Tools you'll use
Some tools perform the same tasks but have different features and levels of automation.
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Automate the model lifecycle, including preparing data, training models, and creating deployments.
Work with R notebooks and scripts in an integrated development environment.
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deploy and run your data science and AI solutions in a test or production environment.
Find and share your data and other assets.
Import asset metadata from a connection into a project or a catalog.
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Measure and monitor the quality of your data.
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Create your business vocabulary to enrich assets and rules to protect data.
Track data movement and usage for transparency and determining data accuracy.
Track AI models from request to production.
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Create a virtual table to segment or combine data from one or more tables.
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
Discover, profile, catalog, and share trusted data in your organization.
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
View, access, manipulate, and analyze your data without moving it.
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Where you work with data.
> Projects > View all projects
Where you find and share assets.
> Catalogs > View all catalogs
Where you deploy and run assets that are ready for testing or production.
> Deployments
Where you manage governance artifacts.
> Governance > Categories
Where you virtualize data.
> Data > Data virtualization
Where you consolidate data into a 360 degree view.
