Enterprises know it is not what problem they solve that makes them successful, but how they solve it. They develop specific approaches which become the secret sauce that sets them apart in their industry. There is no question that AI and machine learning (ML) are the new frontier for innovation. The future of every enterprise company will hinge on their ability to apply their unique “secret sauce” approaches to create and adapt to the emerging AI world.
This emerging world runs on data. Learn How to Properly Prepare Your Data for Supervised Learning in our latest article.
Unlike programming, machine learning models do not operate on a rules-based system where a series of ‘if/then’ statements determine outcomes (e.g. ‘If a car rolls past this point on a red light then automatically send them a ticket’). Instead ML models examine statistical relationships between data points in a data set with defined outcomes, and then apply what they learned about those relationships to analyze and predict outcomes for new data sets.
Every unique use case requires training data that is configured to meet the project’s specific needs. A video annotation project, for example requires a data labeling platform that is nimble enough to classify individual frames as well as label any action occurring among or between various target objects. And labeling CV training data requires an array of tools including keypoint, polygon, bounding box, object detection and classification, parts ID and landmark detection, instance and semantic segmentation, as well as actions and interaction identification.
Implementing machine learning
Implementing ML is no small feet, because it requires both great architecture and high-quality training data. Unlike building enterprise ML architecture, which is ideally done internally in order to integrate with the enterprise’s operating system, the onerous task of labeling data can and should be outsourced. According to Forbes, data scientists spend around 80% of their time on preparing and managing data, 76% of whom say data preparation is the least enjoyable part of their work. Building a team of data scientists and machine learning engineers is difficult enough, given the small pool of qualified candidates. Why waste their talents labeling data?
There is a better solution! We've developed a state-of-the-art data labeling platform that combines human and machine intelligence to produce ground truth data for enterprise AI and ML projects. Our team is made up of industry leading domain experts and a global on-demand workforce of trained data specialists to produce high-quality training data at any scale.
For more information, check out our latest piece, How to Properly Prepare Your Data for Supervised Learning.