Presented by Appen
If there’s one thing that companies large and small can agree on, it’s that deploying effective artificial intelligence (AI) is challenging. Not every organization has the funds, specialized teams, and annotators required for a large-scale AI deployment, and even those that do struggle with collecting enough high-quality data to build accurate models quickly, or update them with the right frequency. Deploying and maintaining AI with speed is essential for a competitive advantage in this rapidly-evolving space, which is why many companies are looking to third-party options that enable them to scale quickly.
In particular, organizations are increasingly relying on off-the-shelf, or pre-built, datasets to provide needed data conveniently with limited risk. These datasets are cost-effective alternatives that can accelerate deployments and provide that last percentage or two of accuracy required to meet desired confidence thresholds. In part two of our five part series on 2021 predictions, we focus on the rise of off-the-shelf datasets.
This trend is one to watch: by 2022, Gartner predicts that 35% of large organizations will be either sellers or buyers of data through online data marketplaces. This is a not-insignificant increase from 25% in 2020.
Off-the-shelf datasets aren’t just for companies with tight budgets, either; large organizations are seeking them out to scale and enhance the performance of their machine learning (ML) models. Using off-the-shelf datasets opens up more options for organizations at a variety of stages in their AI journeys.
Instant datasets: The benefits and the risks
Speed is perhaps the biggest selling point for choosing off-the-shelf datasets: companies no longer need to devote time and resources to collecting and vetting data, a step that takes up a considerable portion of a project. The longer a solution takes to go to market, the less chance it has of gaining a competitive edge, and these datasets enable companies to bring their product to market faster and with more confidence.
Pricing of pre-built datasets is also advantageous because you only pay for what you need. Why is that such an important differentiator? Companies building AI may collect tons of internal data that ends up being unusable for a variety of reasons. They’re paying not only the evaluation of that data to determine whether it’s needed or not, but also to then collect even more data to make up for those missing units. With off-the-shelf solutions, the price advantage is full transparency, paying only for the required units of data, as well as guaranteed accuracy and less variance risk than when collecting and annotating from scratch.
Pre-built data is generally safer than data that has been collected internally because the data has been vetted and guaranteed to be compliant with privacy standards. This area is especially crucial for companies that naturally collect customer data as part of each transaction, as the line of implied consent with that data can get blurry fast. With off-the-shelf solutions, there’s a lower chance of privacy concerns.
As with any third-party-provided product, instant data doesn’t come without risks. Using a third-party vendor means a company will have less control over the process and the solution. This can be partially mitigated by selecting a data partner that’s transparent about where their data is sourced from and provides clear expertise and regulation along the way, or by asking for sample data before making a purchase. Companies also won’t have intellectual property over the data used, which could matter to some organizations.
Off-the-shelf datasets may offer fewer opportunities for customization to specific use cases and edge cases. In some situations, this may require the company to supplement the pre-built datasets with internal data, or use a combination of off-the-shelf data sources. By selecting a data partner with data collection and annotation capabilities, these pre-built datasets can be customized while still keeping cost and time down.
When to use an off-the-shelf solution
While off-the-shelf data can be used for a cold start — that is, teams starting from scratch — a surprisingly large number of companies use it for amplifying existing ML models. In these cases, pre-built datasets can help increase accuracy and fine-tune the model. Recent research discovered that achieving the last few percentage points of accuracy in a model requires exponentially more training data than the first 95%. To achieve those higher percentage points, a company may choose off-the-shelf datasets for that final push.
Many organizations leverage internal data collected during customer transactions to build out their AI solutions. To enhance the customer experience even further through new use cases, however, these organizations may seek out pre-made datasets to serve as a new data source.
Off-the-shelf data can also be used for testing purposes, brought in to check if an AI model is providing the service it was created for, and to course-correct if there are any shortcomings. It can effectively benchmark third-party services as well to help companies determine solutions that best fit their needs.
In its many use cases, off-the-shelf datasets help shorten the process of deployment and fine-tune live models to ensure they’re behaving as efficiently and accurately as possible.
Selecting the right training data vendor remains critical
As AI training data offerings evolve, it will become increasingly vital for companies to evaluate these potential partners with careful scrutiny. In the case of off-the-shelf datasets, companies should select a transparent data partner that provides insight into privacy terms of the data, such as where the collection happens and what consent is given for the data being used. The data partner should be compliant with the highest levels of privacy and security.
Regardless of where a company is at in its AI journey, or what resources it has behind it, off-the-shelf solutions can provide needed dexterity. We expect to see widespread usage of these solutions in the next several years as the AI market accelerates. The pressure to scale quickly and gain a competitive edge will result in a higher prevalence of aggregated methods, where companies use a strategic mix of internal and external resources to deploy with confidence.
At Appen, we have spent over 20 years annotating and collecting data using the best of breed technology platform and leveraging our diverse crowd to help ensure you can confidently deploy your AI models. Solve the cold-start problem for AI models or improve your existing models by using our growing list of off-the-shelf datasets that come with guaranteed accuracy and pricing.
Prithivi Pardeep is VP of Solutions Architect at Appen.
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