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4 Ways Off-the-Shelf AI Training Data Can Benefit Your AI Project

Natural language processing (NLP) helps machines to understand, interpret and process human language. It combines computational linguistics, computer science, cognitive science and artificial intelligence to perform a multitude of tasks from translation and speech recognition to automatic summarization, topic segmentation and more. It’s why so many organizations are developing their own AI initiatives: to improve customer service, automate tasks and streamline marketing.  

Ultimately, NLP is the link that allows for seamless interaction between humans and machines. However, when it comes to developing accurate artificial intelligence models based on NLP, machine learning teams need a great deal of time to collect, annotate and validate the data required.  

This doesn’t bode well for enterprises aiming to launch their products into the marketplace quickly. In cases where market penetration is time sensitive, off-the-shelf AI training data could be a viable alternative to custom data, especially when the data is collected, annotated and validated specifically for NLP projects.  What is off-the-shelf training data? It is AI training data that has already been collected and cleaned up and is ready to use immediately after purchase.

However, in the development of truly accurate, natural and fluent AI, the value of custom data cannot be underestimated. So, when is off-the-shelf AI training data useful?

Let’s look at the four ways ‘canned’ data can benefit organizations and give them the competitive edge in developing high-performing NLP and other AI models and systems.  

1. Testing, Evaluating and Benchmarking

Testing and evaluating a model for accuracy and efficiency is a key step in creating a successful AI model. To ensure the machine learning model is functioning as envisaged, it should be exposed to new, previously unseen data and tested for performance. 

One should never evaluate a model with the same data used to train it, as the model will simply remember the training set and provide the correct output (overfitting). This produces an inaccurate picture of how the model will perform when put up against real-world, uncontrolled conditions.

However, the length of time it takes to collect, annotate and validate a new set for AI training purposes can delay the launch of the product, and potentially lose momentum in the market. Models need to be developed and deployed immediately, to keep up with rapid growth and quick advancements being made every day.

In this scenario, high-quality off-the-shelf AI training data can provide an economical and convenient alternative. Enterprises can use off-the-shelf data to test if their AI models are providing the service they were created for, allowing engineers to correct any shortcomings. 

Alternatively, off-the-shelf data can be used to effectively benchmark third-party cognitive services, to ascertain which service is best suited to your needs.  

2. AI Starter Kit 

Some organizations simply lack the resources and time to build custom models completely from scratch. And in some cases, a fully customized model isn’t needed to effectively automate a portion of tasks or bring other benefits to the business. In this case, digital transformation and AI teams may decide to take advantage of other services that allow developers to build models by coding in Python, or assemble AI models by using pre-built chunks of code. With easily accessible, high-quality datasets used for training, these basic models can be quickly deployed to drive company goals.   

3. Rapid iteration 

In order to achieve their goal of rapid product deployment, machine learning engineers can’t spend weeks or months fine-tuning their products. The longer a product takes to get to market; the less chance it has of gaining the competitive edge.  

Rapid iteration can shorten the process of deployment and allow engineers to update already-live models to keep them as efficient and accurate as possible.  

Off-the-shelf data can assist machine learning teams by giving them the data they need to launch generic models quickly, or to update generic models that are already in production with newer topics and language.  

4. Expansion & Improvement 

Many organizations rely on internal customer data to fuel their AI initiatives. However, if the organization wishes to become more sophisticated in its personalized marketing attempts, for example, it will need to look at expanding or improving its existing models with new data sources.  

By supplementing internal customer data with external “new” data, businesses can drive multiple use cases, such as optimizing marketing spend, enhancing the customer experience, and improving cross-sell and up-sell opportunities.  

New data can be used to expand existing models to function in new domains, speak more languages, or simply become more accurate, up to date and efficient.  

Speed your time to market with data you can trust 

For companies looking to speed their time to market,‘s Marketplace is a valuable resource.   

This online marketplace enables customers to browse a diverse and dynamic online library of AI datasets, available in multiple languages, domains, and recording types, and instantly request samples or request to purchase one or multiple datasets for immediate use. 

“Machine learning teams building AI models have always faced one particularly pressing problem, and that is continuous access to highly accurate data. When large enterprise tech firms want to launch their AI initiatives into the market quickly, they simply don’t have the time to collect and validate the data required to do so. aims to solve this problem by providing customers with access to an extensive library of speech datasets  that will rapidly accelerate their AI programs,” said’s VP of product management, Andrew Webb.   

By May 2022, the library will offer over 25,000 hours of both scripted and spontaneous recordings in English, Italian, Portuguese, German, French, Dutch, Spanish, Hindi and Japanese. Domains will include healthcare, entertainment, hospitality, automotive, generic, banking, insurance, telecom, retail and IVR.  

“Whether you’re building a prototype, or minimum viable product; evaluating or benchmarking current models; building synthetic datasets; or simply needing quality data fast, our continually updated library of datasets will help you  quickly achieve your AI goals,” concluded Webb.  

When it comes to AI training data, many advantages clearly go to customized data that is specifically tailored to the model and use case. However, there are many situations in which off-the-shelf data proves itself to be an effective solution to help get a model off the ground and into the market quickly and effectively. As long as the off-the-shelf AI training data is high-quality, well-annotated and diverse, it will bring many benefits to an NLP model.


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