Artificial Intelligence (AI) and machine learning models require access to high-quality training data in order to learn. It is important to understand the processes of effectively collecting, developing, and testing data as it helps to unleash the full potential of AI.
AI and Machine Learning are some of today’s fastest growing technologies. Many companies around the world are working to deliver applications that harness the power of AI to automate a wide variety of processes, and are using AI to increase their efficiency. To power AI models based on machine learning principles, a training data set is typically used to support machine learning process with reading or identifying a specific kind of data. This data is available in multiple formats including text, number, image, and video formats, to predict learning patterns.
Simply put, machine learning algorithms learn from data. They identify relationships, generate understanding, make decisions, and evaluate their decisions based on the training data they are assigned. The better the training data is, the more accurately the model executes its job. In short, the quality and quantity of the machine learning training data determines the level of accuracy of the algorithms, and therefore the effectiveness of the project or product as a whole.
AI datasets typically present in rows and columns, with each row containing an observation. This observation can be in the form of text, an image, or a video. It is not enough for your dataset to contain a large amount of well-structured data, unless these data have been labeled in the required way.
For example, self-driving vehicles do not only need pictures of the road, but they specifically need labeled images where important elements such as cars, bicycles, pedestrians, street signs are annotated. Another example would be with chatbots, which require entity extraction and high-quality syntactic analysis, not just raw language data.
In short, the data used for training usually needs to be accurately labeled or enriched. There might also be the need to collect more data to power the algorithms.
To decide how much machine learning training data is needed, you need to consider various factors.
The first one would be the importance of accuracy. For some algorithms, it is enough to have an accuracy rate of about 85 - 90%, while for more complicated algorithms, a higher accuracy rate would be required.
In general, use cases that are more complex usually require more data than ones that are less complicated. The more classes you want your model to identify, the more examples it will need for that task.
More and higher quality training data definitely improve your models. More training data means more information for your models and therefore a higher accuracy level, which is always needed especially for large-scale business practices.
Machines don’t see things as humans do. For example, when looking at a picture, we recognize that it shows a carrot. However, a machine would only see a series of pixels that has colors of orange and little bit of green, until it is given enough labeled images that tell it these specific pixels create an image of a carrot.
This is why the most efficient way to prepare the features and labels of training data so that models work successfully is to use human power. Typically, there is a need for a diverse group of annotators, even field experts in some cases, who do the job of labeling data correctly and efficiently. Besides labeling data, humans also help with verifying or correcting a machine’s output, for example ‘Yes, this is a carrot.’ This is called ground truth monitoring and belongs to the iterative human-in-the-loop process.
The more accurate training data labels are, the better the model will perform. Therefore, it is always ideal to find a partner that can take care of the often time-consuming data labelling process by offering data annotation tools and crowd workers. StageZero Technologies is a reliable partner.
In most cases, the process of building a model requires dividing labeled datasets into training and testing sets, training algorithms, and evaluating their performance.
When the validation set’s results are not what you are aiming for, you might need to update weights, add or remove labels, test out different methods, and retrain your model.
During this process, it is vital that your datasets are split in the exact same way, since this is the most efficient way to evaluate success: you are able to observe the labels and decisions which have been improved and which were unsuccessful. Using the same training data sets helps you to ascertain whether you are really improving or not.