Buyer’s Guide for
and Data Labeling
So you want to start a new AI/ML initiative and now you’re quickly realizing that not only finding high quality training data but also data annotation will be few of the challenging aspects for your project. The output of your AI & ML models is only as good as the data you use to train it – so the precision that you apply to data aggregation and the tagging and identifying of that data is important!
Where do you go to get the best data annotation and data labeling services for business AI and machine learning projects?
It’s a question that every executive and business leader like you must consider as they develop their roadmap and timeline for each one of their AI/ML initiatives.
This guide will be extremely helpful to those buyers and decision makers who are starting to turn their thoughts toward the nuts and bolts of data sourcing and data implementation both for neural networks and other types of AI and ML operations.
This article is completely dedicated to shedding light on what the process is, why it is inevitable, crucial factors companies should consider when approaching data annotation tools and more. So, if you own a business, gear up to get enlightened as this guide will walk you through everything you need to know about data annotation.
Let’s get started.
For those of you skimming through the article, here are some quick takeaways you will find on the guide:
- Understand what data annotation is
- Know the different types of data annotation processes
- Know the advantages of implementing this process
- Get clarity on whether you should build your own data labeling models or get them outsourced
- Insights on choosing the right data annotation too
Who is this Guide for?
This extensive guide is for:
- All you entrepreneurs and solopreneurs who are crunching massive amount of data regularly
- AI and machine learning or professionals who are getting started with process optimization techniques
- Project managers who intend to implement a quicker time-to-market for their AI modules or AI-driven products
- And tech enthusiasts who like to get into the details of the layers involved in AI processes.
The Rise of Data Annotation and Data Labeling
The simplest way to explain the use cases of data annotation and data labeling is to first discuss supervised and unsupervised machine learning.
Generally speaking, in supervised machine learning, humans are providing “labeled data” which gives the machine learning algorithm a head start; something to go on. Humans have tagged data units using various tools or platforms such as ShaipCloud so the machine learning algorithm can apply whatever work needs to be done, already knowing something about the data it’s encountering.
By contrast, unsupervised data learning involves programs in which machines have to identify data points more or less on their own.
Using an oversimplified way to understand this is using a ‘fruit basket’ example. Suppose you have a goal to sort apples, bananas and grapes into logical results using an artificial intelligence algorithm.
With labeled data, results that are already identified as apples, bananas and grapes, all the program has to do is make distinctions between these labeled test items to correctly classify the results.
However, with unsupervised machine learning – where data labeling is not present – the machine will have to identify apples, grapes and bananas through their visual criteria – for example, sorting red, round objects from yellow, long objects or green, clustered objects.
Analyzing the Advantages of Data Annotation
When a process is so elaborate and defined, there has to be a specific set of advantages that users or professionals can experience. Apart from the fact that data annotation optimizes the training process for AI and machine learning algorithms, it also offers diverse benefits. Let’s explore what they are.
More Immersive User Experience
The very purpose of AI models is to offer ultimate experience to users and make their life simple. Ideas like chatbots, automation, search engines and more have all cropped up with the same purpose. With data annotation, users get to have a seamless online experience where their conflicts are resolved, search queries are met with relevant results and commands and tasks are executed with ease.
They Make Turing Test Crackable
The Turing Test was proposed by Alan Turing for thinking machines. When a system cracks the test, it is said to be at par with the human mind, where the person on the other side of the machine wouldn’t be able to tell if they are interacting with another human or a machine. Today, we are all a step away from cracking the Turing Test because of data labeling techniques. The chatbots and virtual assistants are all powered by superior annotation models that seamlessly recreate conversations one could have with humans. If you notice, virtual assistants like Siri have not only become smarter but quirkier as well.
They Make Results More Effective
The impact of AI models can be deciphered from the efficiency of results they deliver. When data is perfectly annotated and tagged, AI models cannot go wrong and would simply produce outputs that are the most effective and precise. In fact, they would be trained to such extents that their results would be dynamic with responses varying according to unique situations and scenarios.
Three Key Steps in Data Annotation and Data Labeling Projects
Sometimes it can be useful to talk about the staging processes that take place in a complex data annotation and labeling project.
The first stage is acquisition. Here’s where companies collect and aggregate data. This phase typically involves having to source the subject matter expertise, either from human operators or through a data licensing contract.
The second and central step in the process involves the actual labeling and annotation.
This step is where the NER, sentiment and intent analysis would take place as we spoke about earlier in the book.
These are the nuts and bolts of accurately tagging and labeling data to be used in machine learning projects that succeed in the goals and objectives set for them.
After the data have been sufficiently tagged, labeled or annotated, the data is sent to the third and final phase of the process, which is deployment or production.
One thing to keep in mind about the application phase is the need for compliance. This is the stage where privacy issues could become problematic. Whether it’s HIPAA or GDPR or other local or federal guidelines, the data in play may be data that’s sensitive and must be controlled.
With attention to all of these factors, that three-step process can be uniquely effective in developing results for business stakeholders.