High-Quality AI Training Data

To successfully deploy AI solutions, you need the right training data, and a lot of it. Partner with us to access the crowd, platform, and expertise needed to generate world-class, reliable training data at scale.

Training data is labeled data used to teach AI models or machine learning algorithms to make proper decisions.

For example, if you are trying to build a model for a self-driving car, the training data will include images and videos labeled to identify cars vs street signs vs people. If you are creating a customer service chatbot, the data may be all the different ways to ask "what is my account balance?" both in text and audio, which is then translated to different languages.

Training data is paramount to the success of any AI model or project. Think of it as garbage in, garbage out. If you train a model with poor-quality data, then how can you expect it to perform? You can’t and it won’t.

You may have the most appropriate algorithm, but if you train your machine on bad data, then it will learn the wrong lessons, fail expectations, and not work as you (or your customers) expect. Your success is almost entirely reliant on your data.

Training data isn’t labeled or collected on its own. Human intelligence is required to create and annotate reliable training data. Our high-quality training data is possible thanks to our:

The AI development process is like a continuous flywheel with data being the connection that makes the flywheel go round. Since it all starts with AI training data, it needs to be top-notch to proceed with an AI-based approach confidently. Whether you’re looking at what went right, what went wrong, or an explanation for what is happening with your model, a large number of problems wind up being identified with the quality, quantity, and completeness with AI training data. After all, continuing the self-driving car example from above, if a model doesn’t know the difference between a car and a street sign, how can it be expected to learn properly? The answer is that it cannot reasonably have this expectation assigned to it.

So how does it impact other parts of the AI development flywheel? When you start training your model, you’ll then want to validate that it is trained correctly. You will need test data to see how it does, and then, likely, you’ll need more training data to further tune your model for areas where the model didn’t or couldn’t make an accurate prediction. Once your model is performing the way you would like, it’s critical to refresh your model regularly to ensure that your model evolves as human behavior does.

The best way to make sure that your model is set up for success is to ensure the defining steps of model development are set up properly. That means getting your AI training data pipeline set up properly. By working with an organization that has a world-leading understanding of AI training data and how to put parameters in place that maximize the speed, efficiency, and quality of your system’s learning capabilities, your AI initiatives will be set up to properly reach your business goals. At Appen, we’ll take the time needed to learn about what you’re doing and what you’d like to accomplish with your model. We recognize that no two organizations follow the same path in their development needs, and we’re here to help you define yours.