How to Build Successful Enterprise Machine Learning Projects
Capture Advantages by Investing in High-Quality Training Data
As enterprises across different industries continue to invest in machine learning, it’s becoming clear that high-quality training data is critical to successful model outcomes. When you need hundreds of thousands of data entries to properly train your model, it’s helpful to remember the old adage, “garbage in, garbage out.”
Since working with dozens of top Fortune 500 companies, we’ve compiled our enterprise customers’ notes from working with not only us, but other annotation providers, into a white paper about building successful enterprise machine learning projects. Our white paper is a guide on what type of infrastructure a data labeling partner can provide to meet the required training data output and accuracy standards needed by enterprise companies. These strategies can, in turn, decrease costs while maximizing time and successful outcomes.
Take a peek inside:
- How To Create Quality Training Data Through Data Labeling
- Achieving High Quality At Scale
- Finding a Platform with Customizable Tooling
- Ensuring Quality Best Practices
- Alegion Platform Solutions
- Machine-learning enabled tools
Work with the Right Data Labeling Service Partner
When it comes to enterprise machine learning, low-quality data is costly and can compromise an enterprise’s bottom line. By investing in building reliable sets of training data, enterprises can enable their machine learning models to make better predictions. All of this is possible with a well-trained algorithm that puts an enterprise’s data to good use.
Data labeling with a platform like Alegion’s creates a strong foundation for machine learning models, accelerates machine learning projects, and increases the accuracy of predictions down the line. With a robust product suite and customizable tooling, Alegion’s data labeling platform helps any enterprise begin taking steps toward sustained business improvements.
