There are hundreds of more efficient methods out there, here are 5 innovative data labeling tools that’ll streamline your AI development process

As an AI enthusiast, I’ve come to realize that the cornerstone of any successful AI project lies not just in sophisticated algorithms or cutting-edge technology, but in the data that fuels them.

In this article, I’ll talk the importance of choosing the right data sources, defining clear labeling guidelines, utilizing the appropriate tools and methods, and the ongoing process of validating and refining your data.

I’ll explore the crucial aspect of managing and organizing your data effectively. These insights are not just theoretical musings; they are the hard-earned lessons from the trenches of AI development, vital for anyone looking to harness the transformative power of AI in their business.

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Choose the right data sources

I wish I knew the importance of data when I started in AI. Experts in the field often talk about “garbage in. garbage out.”

The moment you are clear on what problem you are solving time comes to focus on the data that will be used to train your model. To keep things simple we will talk about the two main categories to place your data.

We’ll name our two data categories “focused” and “general”. Focused data is needed to train the core functionality of your model. For example, if you want to recognize puppies it only show perfect pictures of puppies.

AI models do not exist in a perfect world though so we need General pictures to account for bad lighting, weird angles, and synthetic images. Making your model “generalized” is critical.

Define clear labeling guidelines

I hate to say this but failing could label your data clearly could result in your business failing.

Businesses must have guidelines and strategies when approaching their data labeling. Thinking through their definitions, rules, and the complete set of examples.

Any degree of variability or “noise” in the training process can result in problems later down the line. Sometimes not manifesting until a model is being used in production.

Clear labeling guidelines keep your training data consistent. If there is even a small amount of noise that is not corrected a 1% variance could manifest in each layer of the neural network. By the time it’s at layer 100 your results are 100% different than the training data.

Use the right tools and methods

The reason we are spending so much time discussing the training pipeline in relation to computer vision is because these steps are critical.

Without selecting the right tools and methods we could setting ourselves for failure. In computer vision labelling small boxes are drawn around images that need to be recognized.

While this is the most common approach, seeming quite simple. There are varying levels of automation that can be applied. Very similar to the levels of automation in training machine learning models.

In addition to completely manual labeling there is a supervised version of labeling where human correct machine labeled images. We can also fully automate this.

Validate and refine your data

You’re going to hate me for saying this but once you label your data you are just starting. One of the cornerstones of AI training is a practice called “benchmarking”.

Every algorithm needs to have clearly defined measures of quality like accuracy, consistency, and relevance. These are often automated as part of our data pipeline.

Periodically these are reviewed by engineers to ensure that a phenomenon called “data drift” has not occurred. If data is not continually validated and refined there is a chance irrelevant data will enter the training set which will negatively impact performance.

At the core of every computer vision strategy is the idea that data needs to be continually maintained and refined to keep algorithms performing.

Manage and organize your data

So here’s why your best labeled data might be your next failure. Poor management of your data.

Even with your algorithm purring and the data being consistently labeled and fed to the model you still need to know where your data is going. Prioritizing security above all else.

If your proprietary is constantly at risk of being stolen or used against your business then you might be focusing on the wrong priorities. But let’s say your data is secure. Your next focus to ensure a secure computer vision pipeline is the organization and management of your labeled data.

This means storing them in a version controlled system. Enforcing a public record of changes and scores of labeled images.