No-code AI is leveling the playing field to allow anyone to use machine learning to automate repetitive, time-consuming tasks.
According to this report by SlashData from 2019, there are roughly 18.9 million people in the world who know how to code. Among that group, 12.9 million are professionals and the rest are hobbyists. That may seem like a large number, but the reality is that professional coders make up only 0.17% of the world’s population, and the vast majority of the world is unable to utilize AI and machine learning to automate repetitive, time-consuming tasks. The productivity potential for millions of business users across the world remains largely unrealized, but nascent technologies like AutoML have taken root to change the AI landscape.
For example, creating a website used to require HTML and CSS skills, but now can be accomplished by small business owners, creatives, and other business users through popular no-code or low-code tools like Dreamweaver, Squarespace, and Webflow. In a similar way, solutions for manual data entry are becoming more and more available for the average business user. Organizations can leverage data-driven solutions without building out a data science team or outsourcing machine learning specialists.
If you simply want to drag-and-drop a massive pile of document files into a program that automatically organizes them into a spreadsheet, then no-code is for you.
Impira helps financial advisors, billing managers, healthcare professionals, and insurance agents easily automate the inflow of documents and forms. Through simple interactions with Impira, users “train” Impira’s custom machine learning models. Impira automatically updates these models in the background so they become more accurate and more helpful as you go. With standard ML technology, this training process can be time-intensive and require machine learning expertise and tons of sample data.
Get a handful (5+) of similar files.
Click the information you need. Impira trains and deploys a new model immediately.
Retrain to correct any incorrect predictions. Impira updates models automatically as you go.
Gather large separate datasets for training, validation, and testing.
Find the best algorithm for your specific task (or build your own from scratch).
Use the training dataset to teach the algorithm how to predict the target.
Evaluate your trained model against the validation dataset to test your algorithm choices. Go back to Research to make any changes.
Use the testing dataset to evaluate your model to get a true measure of its accuracy.
Go back to the Training step and start again.
No-code software puts users in the driver’s seat of a massively powerful vehicle with a suite of advanced tech under the hood. Just like drivers don’t need to be mechanical engineers to get from Point A to Point B, business users can drive their automation processes for their organizations with unprecedented ease.
If you pop Impira’s hood, you’ll see Impira AutoML taking care of automating the end-to-end process of applying machine learning to your business problems. This allows any business user to simply and easily create models to accelerate your workflow without having to write code.