LyGuide Series: Data Labelling

I once read that the difference between Artificial Intelligence (AI) and human intelligence is that we have data. This is true: AI models require enormous amounts of data for supervised training. It takes more than a good algorithm to create an accurate model, most importantly we need the right data. Accessing the right data in sufficient amounts can be the biggest hurdle to overcome when building machine learning models. Once you've trained your model and start using it, the quality control process needs to be monitored so that any changes are well-timed, effectively communicated and fully understood. The subjective nature of labelling data tends to mean that more manual involvement is required within this process

Data availability for training Artificial Intelligence algorithms is a major challenge to overcome.

Data is the key to creating accurate AI models. The more data you have, the better your model will be. But how do we get more data? The answer is simple: get people to label it!

Unfortunately for us, this can be tough if there aren't enough people willing to help out with our labeling efforts (and how many times have we told ourselves "I'll label some images after lunch"?). This has led to a lot of companies offering incentives such as cash rewards, gift cards or even pizza parties so that more people will participate in their projects and increase their chances of success.

AI models require enormous amounts of data for supervised training.

If you're building an AI model, data is your friend. The more data you have, the better it will be.

The more training examples you can provide for a machine learning algorithm, the faster your model will learn how to perform its task. This means that if you have enough labelled images of cats and dogs respectively (or any other classification task), then instead of having to train on millions of samples like before - which would take ages - now we can just use them as labels instead!

This greatly reduces training time: instead of taking weeks or months in order for our models' accuracy rates in classifying images correctly into categories such as "cat" vs "dog", we could now get there within days or even hours!

It takes more than a good algorithm to create an accurate model, most importantly we need the right data.

If you're like me, you've heard the term "data labelling" thrown around a lot but don't know what it means. Well, I'm here to tell you that data labelling is a crucial part of machine learning and can make or break your model.

If we want our algorithms to perform well in the real world and not just on paper or in simulations then we need good quality data which contains information about how well each label matches with its class (i.e., apple vs orange). For example: We have an image of an apple tree with apples hanging from its branches and another image of an orange tree with oranges hanging from its branches; both images are labeled correctly so when we feed them into our algorithm it will know exactly what kind of fruit grows on each type of tree without being confused by another type of fruit that looks similar but isn't actually found on either tree (e.g., peaches).

Accessing the right data in sufficient amounts can be the biggest hurdle to overcome when building machine learning models.

Accessing the right data in sufficient amounts can be the biggest hurdle to overcome when building machine learning models. Data needs to be labelled, which is time consuming and subjective. There are many ways to get data--from scraping websites or APIs, to crowdsourcing via Mechanical Turk or other platforms that allow people from around the world (and outside your company's firewall) to contribute their time and effort for money. The problem with these methods is that they can often result in bad quality data because there aren't enough reviewers checking each submission before it goes into production workflows; thus, errors creep into your model training process early on when they're easier to fix than later down stream when they become harder and more expensive fixes due lack of information about what went wrong originally!

Once you've trained your model and start using it, the quality control process needs to be monitored so that any changes are well-timed, effectively communicated and fully understood.

Once you've trained your model and start using it, the quality control process needs to be monitored so that any changes are well-timed, effectively communicated and fully understood. This is especially important when dealing with a large number of data sets that may contain different types of errors or anomalies.

The first step is to determine how often it's necessary to perform this monitoring - ideally, this should be done at regular intervals (e.g., weekly). The second step involves identifying what metrics will be used as part of your quality control strategy; these metrics should provide insight into how well your models are functioning overall - not just whether they're performing as expected within their specific domain(s). The third step consists of monitoring those metrics regularly over time: if any problems arise during this process then further investigation into why might need taking place so that appropriate measures can be put in place before things go wrong further down the line!

The subjective nature of labelling data tends to mean that more manual involvement is required within this process.

The subjective nature of labelling data tends to mean that more manual involvement is required within this process. As such, it can be difficult for a machine learning algorithm to learn from the labels given by humans because they are not always consistent or accurate.

Data availability is a huge challenge but it doesn't have to stop you from creating an accurate AI model.

As you can see, the limitations of a data scientist's knowledge and experience are not just a problem for you; they are also a problem for your AI model.

What can be done about this? Well, there are two things: firstly and most importantly, make sure that you understand what your data looks like before starting on an AI project. Using our example above (where we were trying to predict how many oranges would be sold) it would have been helpful if we had known how many oranges were actually sold in previous years so that we could compare it with our predictions later on. Secondly (and much more simply) try not to overcomplicate things at first by making sure that the quality control process is monitored closely so as not to introduce errors into training sets which may then lead onto bigger problems further down the line when trying out new models or techniques such as transfer learning etc..

Conclusion

Data labelling can be a difficult, time-consuming process but it doesn't have to be. With the right tools and processes in place, you can ensure that your data is labelled accurately and quickly without sacrificing quality control.