Data is the basis of innovation across many fields. It’s the fuel that powers machine learning which, in turn, is driving the fourth industrial revolution. The rise of big data presents a huge opportunity for organizations across every sector and nearly every product or service imaginable. But while the opportunity is there, deriving insight and value from big data is very hard to do without the right machine learning model.
Many organizations are turning to machine learning, but some common challenges and pitfalls remain when training the model. One of the biggest challenges stems from the fact machine learning is hugely dependent on having the right quantity and quality of data input. For a machine learning project to deliver real value, it needs vast amounts of high-quality data. This is critical for achieving a high level of accuracy in your AI projects. We’re generating multiple petabytes of data every day, but the problem is that over 90% of this data is unstructured.
Data preparation and cleaning is easily the most time-consuming part of any AI project, and 63% of our recent Data Prep webinar attendees said it is a big challenge. Aside from the sheer quantity of data today’s organizations have at their disposal, there’s also the problem that it comes from many different sources and in countless formats. You can’t simply feed all this information into a machine-learning platform and expect it to work. It needs to be cleaned and labeled to make it useful.
Preparing data for machine learning
Machine learning can automate routine decisions or actions, such as identifying whether a document is an invoice or a purchase order, and then process it accordingly. Humans still, and will continue to, play a critical role in preparing data to train and maintain the machine learning models that do the work.
Here are five essential tips for data preparation that you cannot afford to ignore:
Locate the right raw material. Collecting data for your machine-learning project can also be very time-consuming. Often, data isn’t readily accessible because it’s siloed in different departments. The data you collect should support your project’s goals, and it will need to remain sufficiently available to train and maintain your models in the future.
Let’s say you’re building an automated invoice-processing system. In that case, your raw materials are the invoices themselves, such as PDF files or optical character recognition (OCR) outputs. You’ll have to teach the model how to identify things like purchase-order numbers, dates, and amounts due.
Find out if you have enough data. There aren’t any hard and fast rules dictating how much data you need to train and test your algorithms, but, in general, the more data you have to label, the better your results.
For example, if you’re building a system to detect nutrition labels on groceries, you may be able to get away with only a thousand samples, since there are only a dozen or so different types of nutrition labels. Autonomous vehicles, by contrast, which need to be able to identify obstacles and pedestrians, need many millions of samples.
Start small, but think big. Preparing large datasets for machine learning production is a time-consuming and cumbersome process.
It’s always better to start small with an iterative exploration of the larger dataset. Start with a subset of your data to develop a proof of concept. The quality of your data preparation is especially important here. The more accurate your data-labeling is during the proof-of-concept stage, the more likely your model is to perform well in production.
Account for the data you need to maintain your model. Simply training the model in the first place isn’t enough – it also needs a steady stream of fresh training data to maintain its accuracy. If it doesn’t, you end up with data drift, where the algorithm’s output loses accuracy and may eventually become useless.
Real-world conditions change all the time. Let’s go back to our example of the invoicing system. Let’s say your company starts doing business abroad, where invoices follow different standards and layouts. In this case, an outdated machine learning solution might have difficulty recognizing and processing them.
Hire the right workforce. If you’re building machine learning models, you’ll need a data annotation workforce to achieve quality at scale. You can establish an in-house team or outsource the data preparation work.
The best outsourced teams have experience with data annotation across multiple use cases, client sizes, and industries. They’ve developed processes and workflow best practices, and they know which annotation and labeling tools are best for a particular task or use case. Teams with expertise understand how to transform complex tasks into workflows that support high-quality data labeling.
As AI and other technological advancements bring about profound change, organizations in every industry are under increasing pressure to adopt process automation strategies that help them compete and innovate. You may already have the data, but it’s what you do with it that counts. Proper data preparation will help set you on the path to success in an era of constant transformation.
At CloudFactory, we’ve been preparing data for machine learning for a decade. Learn more about our professionally managed teams for data annotation.
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