When you’re thinking about starting a business, you have to go through many steps to convert a business idea into a reality. But, most of the time, some specialists consider models or a wireframe that can prove that the idea is valuable and relevant. In that case, you will have to face risks when you only depend on a wireframe. This is why you should build a proof of concept (POC).
So, in this article, we will cover some tools that can help to transform a business idea into a proof of concept. You must always consider this step before you create a software prototype.
Dataiku is nothing but a platform that democratizes data access and aids organizations in finding their own path to Artificial Intelligence (AI). While the platform offers numerous capabilities, the team members can focus on the requirements with the latest technologies in mind. But, as far as machine learning is concerned, Dataiku AutoML comes with many pre-built models, statistical functionalities, and several capabilities. With the support of many tools, you can always think Dataiku is ideal for creating a proof of concept.
AWS – SageMaker
As developers use Amazon’s SageMaker, it helps to build Machine Learning (ML) models and deploy them in the future. Through a vast collection of built-in models, the team can focus on developing a product instead of improving the models every now and then. Besides, when considering the Autopilot mode, you can interpret the data and select the most suitable model after accessing the available ones. In addition, the users can also think about creating their own models with the help of various tools.
Azure Machine Learning
While developers and data scientists are always seeking productive experiences, Azure ML helps build and deploy models. Moreover, the services also enhance collaboration within the team. With the latest tools, the services later help to create models effectively. Through assisted labeling, your team can also present the data quickly. But, when you have to process certain solutions, you can use the auto-scaling feature and share GPU and CPU clusters. To improve the overall accuracy of the models, you can also think about using cognitive APIs. The best part of using the services is that proof of concept specialists can build POCs much better, even with the free or the trial version.
Apart from helping professionals assess a particular model, Google Colab can be helpful in training image classifiers and importing datasets. Furthermore, you can also utilize Google hardware regardless of the machine. While the tool includes an extensive range of Python libraries, you rarely have to consider installing any additional ones. In addition, when you write the code, it always stays secure when you store it on Google Drive. But, if the data crosses 15 GB, you have to consider paying additional to get more space on the drive.
When you start using Jupyter, you can always develop open-source applications at a certain pace. Currently, you can also use some of the capabilities that are available with Jupyter. These include the Jupyter Notebook, Jupyter Hub, Voila, and Jupyter Lab. On the other hand, as soon as you install the packages, you will not have to install them again. You can also document the code easier with the services through the cell-based approach. The development environment also has a modular structure which can help you organize many notebooks easily. The Jupyter Hub can later enhance the functionality of the notebooks and can be customized depending on the overall size of the team.
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It’s always better to get in touch with teams specialized in developing POC like the TechRivo team, which will offer the most effective way to build a POC. While you discuss the idea in detail, the team will ensure to fulfill everything right to the end. But, as you prove the proof of concept, the team will work towards building a software prototype. Once you observe the prototype, you will always get an idea about how the product will work once is fully developed.