While learning from experience is natural for the majority of organisms—even plants and bacteria—designing machines with the same ability requires creativity, experimentation, and persistence. And yet the potential for machine learning is limitless.
A number of the most anticipated applications for machine learning have sprung from the subfield of computer vision. Regardless of whether we’re talking about unmanned drones, self-driving cars, or computer-assisted surgery, nothing is more essential for an AI application than quality machine learning datasets.
After all, machine learning without sufficient training data is impossible. But how do these data sets work, and what impact do they actually have on machine learning?
What Is a Training Dataset in Machine Learning?
It’s important that we understand what a training dataset is in the first place before we discuss why machine learning leans so heavily on its initial training data. Training datasets for machine learning projects are collections of data that are fed into algorithms to create a predictive model.
Machine learning models represent problems in the real world using mathematical expressions—these expressions, called algorithms, need data to dictate and refine their internal set of rules.
The quality of your training data has immense implications for your model’s development. We’ve all heard the motto of machine learning, “garbage in, garbage out.” Let’s talk about why it’s true.
Fueling Your Algorithm: Training Datasets for Machine Learning Models
Autonomous models aren’t created magically—they require powerful algorithms. Machines use these algorithms as a guide for decoding the world around them, and these algorithms need data. Without quality data, even the highest functioning algorithms are all but useless.
Think of a weather application. Without the right data—which would include atmospheric conditions, cloud patterns, and recent weather—the application couldn’t formulate a reliable weather forecast for the upcoming week, regardless of how accurate such a forecast could be. Without the right data, any forecast it produces is meaningless.
Trained data scientists, proficient algorithms—none of that matters for your machine learning project if your data is raw, unstructured, low-quality, or inaccurate. In essence, training data is the foundation for every machine learning model.
Just like weather applications call for weather-related data, every computer vision application requires its own type of data. Now we’re ready to discuss the core process involved in successful computer vision—data labeling for machine learning.
Giving a Computer Eyes: Data Annotation in Machine Learning
Data annotation is exactly what it sounds like—annotating, or labeling, data to be fed into machine learning algorithms, which ultimately allow machines to make sense of their surroundings.
Different methods of image and video annotation are used to create the optimal training datasets for machine learning projects. Among the leading methods are bounding box, skeletal, polygon, and landmark point annotation.
For a variety of reasons—including drastically increased speed, accuracy, and quality—AI solutions usually rely on professional data annotation services. Custom image object annotations services have the technology and trained staff to produce high-volume, pixel-perfect training datasets in less time and at scale.
Finding Relevant, Accurate, and High-Quality Datasets
What if you don’t have your own data? Searching for the right training dataset on the web or through individual data vendors is a hassle and uniquely frustrating for AI companies with highly specific or high-volume needs.
Keymakr makes data collection and annotation easy. Simply tell us what type of data your machine learning project requires, and we’ll collect and annotate it. And if your data can’t be found, our production studios will be the first to create it.
At Keymakr, we have the team, tools, and techniques to deliver the first-rate training data your machine learning model craves. We can collect, create, and annotate your data exactly as you would—if you had the time.
Are you interested in learning more about how we can help you better train your algorithms? Get in touch with a member of our team to book your free demo.