There is an ongoing argument for and against using the off-the-shelf dataset to develop high-end artificial intelligence solutions for businesses. But off-the-shelf training datasets can be the perfect solution for organizations that do not have a specialized in-house team of data scientists, engineers, and annotators at their disposal.
Even if organizations have teams for large-scale ML deployments, they sometimes have trouble collecting the high-quality data required for the model.
Moreover, speed of development and deployment is necessary to gain a competitive advantage in the market, forcing many companies to rely on off-the-shelf datasets. Let’s define off-the-shelf data, and understand their benefits and considerations before deciding to go for them.
What are Off-the-Shelf Datasets?
An off-the-shelf training dataset is a viable option for companies looking to quickly develop and deploy AI solutions when they do not have the time or the resources to build custom data.
Off-the-shelf training data, as the name suggests, is a dataset that has already been collected, cleaned, categorized, and ready for use. Although the value of custom data cannot be undermined, the next best alternative would be an off-the-shelf dataset.
Why and when you Should Consider Off-the-Shelf Datasets?
Let’s start by answering the first part of the statement—the ‘why.’
Perhaps the biggest advantage of using an off-the-shelf training dataset is its spoed. As a business, you no longer need to spend significant time, money, and resources developing custom data from scratch. The initial data collection and vetting steps take up much of the project time. The longer you wait to deploy a solution into the market, the less chance it has of making it big due to the competitive nature of the business.
Nog 'n voordeel is die pryspunt—pre-built datasets are cost-effective and ready. Think of it for a second: a business building an AI solution will collect massive amounts of internal and external data. However, not all the collected data is used to develop applications. Additionally, the company will not only be paying for the data-insameling but also for evaluation, cleaning, and rework. With off-the-shelf datasets, on the other hand, you only have to pay for the data used.
As there are guidelines for data privacy, off-the-shelf data is generally a safer and more secure dataset. However, with instant data, there are always going to be risks involved, such as less control over the data source and a lack of intellectual property rights over the data.
Now let’s tackle the next part of the statement: "wanneer" to use a pre-built dataset?
Outomatiese spraakherkenning
ASR, or Automatic Speech Recognition, is used to develop various applications such as voice assistants, video captioning, and more. However, developing an ASR-based application requires massive amounts of annotated data and computing. When you add language diversity to the mix, acquiring the needed dataset to train the ML models becomes challenging.
Masjienvertaling
Accurate machine translation paves the way for enhanced customer experiences and requires high-quality datasets for training. You need large quantities of accurately annotated language data to develop a credible and reliable machine translation application.
Teks-na-spraak
Text-to-speech assistive technology is used for in-car systems, virtual assistants, and mobile phones. The TTS-based application can be developed when the ML algorithm is trained on high-quality annotated data.
Benefits of Off-the-shelf Training Datasets for ML Projects
Aids in Faster and more Accurate Training and Testing
Testing and evaluation are the keys to developing high-performing ML solutions. To ensure the model delivers reliable predictions, it should be tested on new and unique data. Evaluating the model on the same data used for testing will not provide accurate results in real-world scenarios.
Yet, it takes a lot of time and effort to collect, clean, annotate, and validate data in a way that doesn’t impact the development and deployment timeframes. In such cases, it is advantageous to use off-the-shelf datasets as they are readily available, economical, and useful.
Gets your AI project off to a Start
Sometimes, AI projects cannot take off simply because they do not have the resources needed to collect data from scratch. Moreover, in some cases, a completely new solution is not required. In such cases, it makes sense to use a pre-collected dataset to test only that portion of the model that’s going to be deployed.
Allows for Rapid Development and Improvement
AI initiatives for businesses are not a one-time fix; rather, they are an iterative process that uses customer data to enhance and improve existing models. Businesses can supplement present data with new data to test several use cases, devise personalized strategies, and improve the customer experience.
Risks of Using Off-the-Shelf Training Datasets for your ML Projects
Using pre-built KI-opleidingsdata might come with many advantages, but it is not without its share of risks.
With off-the-shelf training datasets, you risk having less control over the information, process, and solution. Since the data in pre-built datasets may be generic, customization options are also quite limited, especially when testing for edge cases. Companies must supplement the existing information with pre-built data to ensure the data is aligned with your business needs.
To truly get the best out of voorbeeld datastelle and mitigate the drawbacks of using pre-built datasets, you must select an experienced and reliable data partner. By choosing a data partner with data collection and annotating data capabilities, you can customize your applications and significantly cut down time-to-market while maintaining high performance.
Shaip has years of experience providing high-quality datasets to businesses using top-of-the-line technologies and an experienced team. We help you kickstart your AI products and get them off to a running start with our well-annotated and dynamic datasets.
