Get the Data Right
For successful machine learning deployment, getting the data right through annotation and from high-quality sources is key. For many teams, this means bringing the data annotation process in-house. And yet, data annotation is a repetitive, time-consuming task. If your data scientists spend time labeling and preparing data, that’s precious time that they could have spent on other projects. Using high-quality data is important, but it’s not cost-effective to bring data labeling in-house for most AI projects. Let your AI team focus on building the AI model, refining the algorithm, and getting ready for deployment. Let someone else create the high-quality data set that you need. While the cost of high-quality data might seem expensive up-front, look at it as a cost-saving measure. By outsourcing your data preparation, you save your team the time it would take to create the data set and properly annotate it. Using the right data at the beginning will also save you time after you deploy your machine learning model. If you don’t spend enough time on the data up-front, you’re likely to run into problems with your algorithm. This can result in expensive model retraining. When your company is already investing a lot of money in an AI project and leaning on it to solve a company-wide problem, you need it to work the first time around. There are a number of real-world examples where cost gets in the way of successful deployment of AI projects. Gartner estimates that only half of all AI projects ever make it to deployment and those that do take an average of nine months. And when it goes wrong, it’s too expensive to fix. OpenAI found a mistake in GPT-3, but the cost of training was so high, that retraining the model wasn’t cost-effective.Get the Right Data
When working with data, the natural assumption is that more data equals better data. But, when you’re training a machine learning algorithm, you’d be smart to follow the old adage: quality over quantity. Using a high-quality, small data set can save you money overall. You can save a little money from your compute budget and reallocate it to purchasing a high-quality, small data set. By using a small, but high-quality data set, you don’t have to spend as much on computing and can avoid having to retrain your model after using an entire organizational data set. You’ll find that buying the right data is money well spent. With more than 75 percent of companies saying they have AI models that never get deployed, it’s smart business sense to spend your money on the right data to get your machine learning model functional and ready for deployment. For a more successful deployment of your machine learning model, follow these steps to getting the right data.Find a High-Quality Data Source
The first thing you’ll want to do is find a data source where you can purchase a high-quality data set. Choosing a reliable source where you know you can get good data that fits your use case is key to successful deployment of your machine learning model. When you’re looking for a dataset to fit your use case, you have a few different options. You can hire a company to create a unique dataset for your use case and company or you can build the dataset yourself. Another option is to find an off-the-shelf dataset. An off-the-shelf dataset is a dataset that has already been put together and is available for use. You can even find open-source datasets, though these are often of less quality or size and may not be enough to support your project. An off-the-shelf dataset is a great option for low-budget projects or for teams that don’t have enough team members to build their own dataset. There are a number of different repositories where you can find a variety of off-the-shelf datasets for your needs. An example of how an off-the-shelf dataset can solve a business problem comes from MediaInterface, a language technology company that operates primarily out of Germany, Austria, and Switzerland. When the company was looking to expand to France, they realized they needed a significant amount of new data — in French. We were able to help them get the data they need with a high-quality, off-the-shelf dataset.Look for Small and Wide Data
While using a large data set to train your machine learning model seems intuitive, using a small and wide data set can actually be more cost-effective and useful in the long run. And, to be clear, a small dataset doesn’t mean a small amount of data. Small data means the right data to solve your problem. Training your machine learning model with a small and wide data set can provide you with more robust analytics, reduce dependency on big data, and deliver a richer, more nuanced algorithm. To create a high-quality, small dataset, you’ll want to focus on:- Data relevance
- Data diversity over repetition
- Building a data-centric model
