An untrained statistical model is like a Ferrari that simply will not run. In other words, it is simply not of much use. Supervised Learning is based on the availability of high quality labeled data. Labeled data is the ingredient that will make your Ferrari ( statistical model) roar. To put it in technical terms, labeling your training data gives your model the ability to correctly predict, classify and otherwise analyze data to generate meaningful output.
Rule of thumb, it’s best not to develop a model if you haven't figured out how to first acquire and then, more importantly, label(‘tag’ or ‘annotate’) a suitable training data set.
Labeling is a tedious and time-consuming affair, isn’t it? Check out these ingenious methods that you can use to get your data set labeled without breaking the bank.
Rely on a Third-Party Plug-and-Play AI Model
It is much easier to use pre-trained models to generate the insights you are looking for. These plug-and-play AI models save you the effort of creating a functional model, labeling and training your data set. All you need is raw data which can be plugged into a well-curated and reliable model which will generate useful metrics for you.
A whole host of such APIs has been developed by the Komprehend team. Using these models is very convenient and result-driven. Some of the standard functionalities addressed within text analysis are - sentiment analysis, emotion detection, keyword extractor, semantic similarity and much more. Within the visual intelligence space, Komprehend has created an object recognizer and facial emotion detection.
Use of Unsupervised learning models
These Machine learning algorithms can learn from unlabeled input data. Unsupervised learning algorithms are a step towards automated machine learning. This approach removes the problem of labeling right from the root. Instead of responding to feedback, unsupervised learning identifies common points in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This branch of Machine Learning is still in its infancy and is yet to be developed to produce results that outperform the supervised learning algorithms in terms of accuracy and scalability.
Unsupervised learning is also termed as Zero-Shot Learning.
Restructuring the Existing Data Set
Transfer learning is the process of reusing some or all of the training data, feature representations, neural-node layering, weights, training method, loss function, learning rate, and other properties of an existing model. This technique is perhaps the most effective strategy to lower the effort, time and cost associated with acquiring labeled data.
Acquire Low-Cost Labeled Data From Open
The World Wide Web is full of untapped data that is waiting to be harvested given you have the right tools. Dozens of these sources can be tapped for data at no cost. All you really need is access to a nifty data crawler and you can acquire a well labeled data set to get things under-way. Beware of not violating any data ownership rights while you are at it though. And remember to revamp the acquired data to suit your needs.
Use service providers who can label your data for you
If the human resource is an issue for your organization there are service providers who can do the grunt work and provide you with well labeled data. These service providers often prove to be inexpensive and reliable. In some cases, outsourcing the data labeling task to commercial service providers proves much more scalable than carrying out the task internally. Some of the players in this space are Scale, DataPure and LabelBox.
Embed Labeling Tasks in Online Applications
Captcha challenges are commonplace on the Internet. One ingenious technique to acquire labeled data can be to leverage these challenges by embedding your training data over them. This technique proves very effective for training text and image recognition models. Another popular technique is embedding your training data on smartphone applications which incentivize its user base to identify, classify or otherwise comment on presented text or image.
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