4 Ways Off-the-shelf 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 training data could be a viable alternative to custom data, especially when the data is collected, annotated and validated specifically for NLP projects.  

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 data useful? Let’s look at the four ways ‘canned’ data can benefit organizations and give them the competitive edge.  

  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. 

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). 

However, the length of time it takes to collect, annotate and validate a new set for training purposes can delay the launch of the product, and potentially lose momentum in the market. 

In this scenario, high-quality off-the-shelf 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 

Off-the-shelf training data can work as a scalable foundation for successful AI products. As more and more large tech companies attempt to democratize AI, more advanced algorithms are becoming easily available. For example, cloud services like Microsoft Azure’s Bot-as-a-Service, allow developers to easily build, connect, deploy and manage intelligent bots. All developers need is the data to train the bots to understand and successfully interact with people.  

Alternatively, other services 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 training datasets, 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, DefinedData 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. DefinedData 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 DefinedCrowd’s VP of engineering, Andrew Michalik.   

By May 2021, 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 Michalik.