Creating Your Next Big Thing? Don't Skip the Prototype Phase with SAS Viya

Attention all data scientists and analysts! If you're starting to create a use case or product, don't overlook the importance of the prototype phase. This stage is crucial for visualizing how your work will look and delving deeper into the process steps and tools you'll need.

If you're working with SAS Viya, an end-to-end analytical platform, you'll be pleased to know that there are a variety of tools available to help you create a prototype that's compatible with the platform. Here are a few options to consider:

• SAS Data Studio: A web-based application that provides a point-and-click interface for data preparation, exploration, and visualization. It's fully integrated with SAS Viya, so it's easy to move data between the two tools.
• SAS Studio: A web-based development environment that provides a code editor and a point-and-click interface for developing SAS code. It's also fully integrated with SAS Viya, making it easy to move code and data between the two tools.
• Jupyter Notebook: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It supports several programming languages, including Python, R, and SAS. With Jupyter Notebook, you can create a prototype of your machine learning model using Python code and the SAS Viya Python API.

SAS Viya is an advanced analytics platform that allows you to perform data preparation, model development, deployment, and monitoring all in one place. It provides a variety of features such as automated machine learning, text analytics, deep learning, and forecasting. With SAS Viya, you can quickly develop, deploy, and scale your analytical models, making it an ideal platform for large enterprises.

When it comes to the prototype phase, it's important to note that there are different types of prototypes you can create, such as low-fidelity and high-fidelity prototypes. Low-fidelity prototypes are simple and quick to create, while high-fidelity prototypes are more detailed and complex. It's also important to follow best practices when creating prototypes, such as starting with a clear objective, involving stakeholders, and testing the prototype with real users.

In conclusion, using tools like SAS Data Studio, SAS Studio, and Jupyter Notebook can help you create a prototype of your machine learning model and data processing workflow, which can then be further developed and integrated with SAS Viya. Don't skip the prototype phase – it's a critical part of the development process that can help you save time, money, and resources in the long run.

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