Data Annotation & Data Labeling
The Ultimate Buyers Guide 2021
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
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.
Read the Data Annotation / Labeling Buyers Guide, or download a PDF Version
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 in the guide:
- Understand what data annotation is
- Know the different types of data annotation processes
- Know the advantages of implementing the data annotation process
- Get clarity on whether you should go for in-house data labeling 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.
What is Machine Learning?
We’ve talked about how data annotation or data labeling supports machine learning and that it consists of tagging or identifying components. But as for deep learning and machine learning itself: the basic premise of machine learning is that computer systems and programs can improve their outputs in ways that resemble human cognitive processes, without direct human help or intervention, to give us insights. In other words, they become self-learning machines that, much like a human, become better at their job with more practice. This “practice” is gained from analyzing and interpreting more (and better) training data.
One of the key concepts in machine learning is the neural network, where individual digital neurons are mapped together in layers. The neural network sends signals through those layers, much like the workings of an actual human brain, to get results.
What this looks like in the field is different on a case-by-case basis, but fundamental elements apply. One of those is the need for labeled and supervised learning.
This labeled data typically comes in the form of training and test sets that will orient the machine learning program toward future results as future data inputs are added. In other words, when you have a good test and training data setup, the machine is able to interpret and sort new incoming production data in better and more efficient ways.
In that sense, optimizing this machine learning is a search for quality and a way to solve the “value learning problem” – the problem of how machines can learn to think on their own and prioritize results with as little human assistance as possible.
In developing the best current programs, the key to effective AI/ML implementations is “clean” labeled data. Test and training data sets that are well-designed and annotated support the results that engineers need from successful ML.
What is Data Annotation?
Like we mentioned earlier, close to 95% of the data generated is unstructured. In simple words, unstructured data can be all over the place and is not properly defined. If you are building an AI model, you need to feed information to an algorithm for it to process and deliver outputs and inferences.
This process can happen only when the algorithm understands and classifies the data that is being fed to it.
And this process of attributing, tagging or labeling data is called data annotation. To summarize, data labeling and data annotation is all about labeling or tagging relevant information/metadata in a dataset to let machines understand what they are. The dataset could be in any form i.e., image, an audio file, video footage, or even text. When we label elements in data, ML models accurately comprehend what they are going to process and keep that information to automatically process newer information that is built on existing knowledge to take timely decisions.
With data annotation, an AI model would know if the data it receives is audio, video, text, graphics or a mix of formats. Depending on its functionalities and parameters assigned, the model would then classify the data and proceed with executing its tasks.
Data annotation is inevitable because AI and machine learning models need to be trained consistently to become more efficient and effective in delivering required outputs. In supervised learning, the process becomes all the more crucial because the more annotated data that is fed to the model, the sooner it trains itself to learn autonomously.
For instance, if we have to talk about self-driving cars, which completely rely on data generated from its diverse tech components such as computer vision, NLP (Natural Language Processing), sensors, and more, data annotation is what pushes the algorithms to make precise driving decisions every second. In the absence of the process, a model would not understand if an approaching hurdle is another car, a pedestrian, an animal, or a roadblock. This only results in an undesirable consequence and the failure of the AI model.
When data annotation is implemented, your models are precisely trained. So, regardless of whether you deploy the model for chatbots, speech recognition, automation, or other processes, you would get optimum results and a fool-proof model.
Why is Data Annotation Required?
We know for a fact that computers are capable of delivering ultimate results that are not just precise but relevant and timely as well. However, how does a machine learn to deliver with such efficiency?
This is all because of data annotation. When a machine learning module is still under development, they are fed with volumes after volumes of AI training data to make them better at making decisions and identifying objects or elements.
It’s only through the process of data annotation that modules could differentiate between a cat and a dog, a noun and an adjective, or a road from a sidewalk. Without data annotation, every image would be the same for machines as they don’t have any inherent information or knowledge about anything in the world.
Data annotation is required to make systems deliver accurate results, help modules identify elements to train computer vision and speech, recognition models. Any model or system that has a machine-driven decision-making system at the fulcrum, data annotation is required to ensure the decisions are accurate and relevant.
Data Annotation VS Data Labeling
There is a very thin line difference between data annotation and data labeling, except the style and type of content tagging that is used. Hence quite often they have been used interchangeably to create ML training data sets depending on AI model and process of training the algorithms.
|Data Annotation||Data Labeling|
|Data annotation is the technique through which we label data so as to make objects recognizable by machines||Data labeling is all about adding more info/metadata to various data|
types (text, audio, image and video) in order to train ML models
|Annotated data is the basic requirement to train ML models||Labeling is all about identifying relevant features in the dataset|
|Annotation helps in recognizing relevant data||Labeling helps in recognizing patterns so as to train algorithms|
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.
The major drawback to unsupervised learning is the algorithm is, in so many key ways, working blind. Yes, it can create results – but only with much more powerful algorithm development and technical resources. All of that means more development dollars and upfront resources – adding to even greater levels of uncertainty. This is why supervised learning models, and the data annotation and labeling that come with them, are so valuable in building any kind of ML project. More often than not, supervised learning projects come with lower upfront development costs and much greater accuracy.
In this context, it’s easy to see how data annotation and data labeling can dramatically increase what an AI or ML program is able while at the same time decreasing time to market and total cost of ownership.
Now that we’ve established that this type of research application and implementation is both important and in demand let’s look at the players.
Again, it starts with the people that this guide is designed to help – the buyers and decision makers who operate as strategists or creators of an organization’s AI plan. It then extends to the data scientists and data engineers who will be working directly with algorithms and data, and monitoring and controlling, in some cases, the output of AI/ML systems. This is where the vital role of the “Human in the Loop” comes into play.
Human-in-the-Loop (HITL) is a generic way to address the importance of human oversight in AI operations. This concept is very relevant to data labeling on a number of fronts – first of all, data labeling itself can be seen as an implementation of HITL.
What’s a data labeling/annotation tool?
In simple terms, it’s a platform or a portal that lets specialists and experts annotate, tag or label datasets of all types. It’s a bridge or a medium between raw data and the results your machine learning modules would ultimately churn out.
A data labeling tool is an on-prem, or cloud-based solution that annotates high-quality training data for machine learning models. While many companies rely on an external vendor to do complex annotations, some organizations still have their own tools that is either custom-built or are based on freeware or opensource tools available in the market. Such tools are usually designed to handle specific data types i.e., image, video, text, audio, etc. The tools offer features or options like bounding boxes or polygons for data annotators to label images. They can just select the option and perform their specific tasks.
Overcome the Key Challenges in Data Labor
There are a number of key challenges to be evaluated in developing or acquiring the data annotation and labeling services that will offer the highest quality output of your machine learning (ML) models.
Some of the challenges have to do with bringing the right analysis to the data you’re labeling (i.e text documents, audio files, images or video). In all cases, the best solutions will be able to come up with specific, targeted interpretations, labeling, and transcriptions.
Here is where algorithms need to be muscular and targeted to the task at hand. But this is only the basis for some of the more technical considerations in developing better nlp data labeling services.
At a broader level, the best data labeling for machine learning is much more about the quality of human participation. It’s about workflow management and on-boarding for human workers of all kinds – and making sure that the right person is qualified and doing the right job.
There’s a challenge in getting the right talent and the right delegation to approach a particular machine learning use case, as we’ll talk about later.
Both of these key fundamental standards have to be put into play for effective data annotation and data labeling support for AI/ML implementations.
Types of Data Annotation
This is an umbrella term that encompasses different data annotation types. This includes image, text, audio and video. To give you a better understanding, we have broken each down into further fragments. Let’s check them out individually.
From the datasets they’ve been trained on they can instantly and precisely differentiate your eyes from your nose and your eyebrow from your eyelashes. That’s why the filters you apply fit perfectly regardless of the shape of your face, how close you are to your camera, and more.
So, as you now know, image annotation is vital in modules that involve facial recognition, computer vision, robotic vision, and more. When AI experts train such models, they add captions, identifiers and keywords as attributes to their images. The algorithms then identify and understand from these parameters and learn autonomously.
Audio data has even more dynamics attached to it than image data. Several factors are associated with an audio file including but definitely not limited to – language, speaker demographics, dialects, mood, intent, emotion, behavior. For algorithms to be efficient in processing, all these parameters should be identified and tagged by techniques such as timestamping, audio labeling and more. Besides merely verbal cues, non-verbal instances like silence, breaths, even background noise could be annotated for systems to understand comprehensively.
While an image is still, a video is a compilation of images that create an effect of objects being in motion. Now, every image in this compilation is called a frame. As far as video annotation is concerned, the process involves the addition of keypoints, polygons or bounding boxes to annotate different objects in the field in each frame.
When these frames are stitched together, the movement, behavior, patterns and more could be learnt by the AI models in action. It is only through video annotation that concepts like localization, motion blur and object tracking could be implemented in systems.
Today most businesses are reliant on text-based data for unique insight and information. Now, text could be anything ranging from customer feedback on an app to a social media mention. And unlike images and videos that mostly convey intentions that are straight-forward, text comes with a lot of semantics.
As humans, we are tuned to understanding the context of a phrase, the meaning of every word, sentence or phrase, relate them to a certain situation or conversation and then realize the holistic meaning behind a statement. Machines, on the other hand, cannot do this at precise levels. Concepts like sarcasm, humour and other abstract elements are unknown to them and that’s why text data labeling becomes more difficult. That’s why text annotation has some more refined stages such as the following:
Semantic Annotation – objects, products and services are made more relevant by appropriate keyphrase tagging and identification parameters. Chatbots are also made to mimic human conversations this way.
Intent Annotation – the intention of a user and the language used by them are tagged for machines to understand. With this, models can differentiate a request from a command, or recommendation from a booking, and so on.
Text Categorization – sentences or paragraphs can be tagged and classified based on overarching topics, trends, subjects, opinions, categories (sports, entertainment and similar) and other parameters.
Entity Annotation – where unstructured sentences are tagged to make them more meaningful and bring them to a format that can be understood by machines. To make this happen, two aspects are involved – named entity recognition and entity linking. Named entity recognition is when names of places, people, events, organizations and more are tagged and identified and entity linking is when these tags are linked to sentences, phrases, facts or opinions that follow them. Collectively, these two processes establish the relationship between the texts associated and the statement surrounding it.
3 Key Steps in Data Labeling and Data Annotation Process
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 stage 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.
Data Annotation Process
Features for Data Annotation and Data Labeling Tools
Data annotation tools are decisive factors that could make or break your AI project. When it comes to precise outputs and results, the quality of datasets alone doesn’t matter. In fact, the data annotation tools that you use to train your AI modules immensely influence your outputs.
That’s why it is essential to select and use the most functional and appropriate data labeling tool that meets your business or project needs. But what is a data annotation tool in the first place? What purpose does it serve? Are there any types? Well, let’s find out.
Similar to other tools, data annotation tools offer a wide range of features and capabilities. To give you a quick idea of features, here’s a list of some of the most fundamental features you should look for when selecting a data annotation tool.
The data annotation tool you intend to use must support the datasets you have in hand and let you import them into the software for labeling. So, managing your datasets is the primary feature tools offer. Contemporary solutions offer features that let you import high volumes of data seamlessly, simultaneously letting you organize your datasets through actions like sort, filter, clone, merge and more.
Once the input of your datasets is done, next is exporting them as usable files. The tool you use should let you save your datasets in the format you specify so you could feed them into your ML modles.
This is what a data annotation tool is built or designed for. A solid tool should offer you a range of annotation techniques for datasets of all types. This is unless you’re developing a custom solution for your needs. Your tool should let you annotate video or images from computer vision, audio or text from NLPs and transcriptions and more. Refining this further, there should be options to use bounding boxes, semantic segmentation, cuboids, interpolation, sentiment analysis, parts of speech, coreference solution and more.
For the uninitiated, there are AI-powered data annotation tools as well. These come with AI modules that autonomously learn from an annotator’s work patterns and automatically annotate images or text. Such
modules can be used to provide incredible assistance to annotators, optimize annotations and even implement quality checks.
Data Quality Control
Speaking of quality checks, several data annotation tools out there roll out with embedded quality check modules. These allow annotators to collaborate better with their team members and help optimize workflows. With this feature, annotators can mark and track comments or feedback in real time, track identities behind people who make changes to files, restore previous versions, opt for labeling consensus and more.
Since you’re working with data, security should be of highest priority. You may be working on confidential data like those involving personal details or intellectual property. So, your tool must provide airtight security in terms of where the data is stored and how it is shared. It must provide tools that limit access to team members, prevent unauthorized downloads and more.
Apart from these, security standards and protocols have to be met and complied to.
A data annotation tool is also a project management platform of sorts, where tasks can be assigned to team members, collaborative work can happen, reviews are possible and more. That’s why your tool should fit into your workflow and process for optimized productivity.
Besides, the tool must also have a minimal learning curve as the process of data annotation by itself is time consuming. It doesn’t serve any purpose spending too much time simply learning the tool. So, it should be intuitive and seamless for anyone to get started quickly.
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.
To build or not to build a Data Annotation Tool
One critical and overarching issue that may come up during a data annotation or data labeling project is the choice to either build or buy functionality for these processes. This may come up several times in various project phases, or related to different segments of the program. In choosing whether to build a system internally or rely on vendors, there’s always a trade-off.
As you can likely now tell, data annotation is a complex process. At the same time, it’s also a subjective process. Meaning, there is no one single answer to the question of whether you should buy or build a data annotation tool. A lot of factors need to be considered and you need to ask yourself some questions to understand your requirements and realize if you actually need to buy or build one.
To make this simple, here are some of the factors you should consider.
The first element you need to define is the goal with your artificial intelligence and machine learning concepts.
- Why are you implementing them in your business?
- Do they solve a real-world problem your customers are facing?
- Are they making any front-end or backend process?
- Will you use AI to introduce new features or optimize your existing website, app or a module?
- What is your competitor doing in your segment?
- Do you have enough use cases that need AI intervention?
Answers to these will collate your thoughts – which may currently be all over the place – into one place and give you more clarity.
AI Data Collection / Licensing
AI models require only one element for functioning – data. You need to identify from where you can generate massive volumes of ground-truth data. If your business generates large volumes of data that need to be processed for crucial insights on business, operations, competitor research, market volatility analysis, customer behavior study and more, you need a data annotation tool in place. However, you should also consider the volume of data you generate. As mentioned earlier, an AI model is only as effective as the quality and quantity of data it is fed. So, your decisions should invariably depend on this factor.
If you do not have the right data to train your ML models, vendors can come in quite handy, assisting you with data licensing of the right set of data required to train ML models. In some cases, part of the value that the vendor brings will involve both technical prowess and also access to resources that will promote project success.
Another fundamental condition that probably influences every single factor we are currently discussing. The solution to the question of whether you should build or buy a data annotation becomes easy when you understand if you have enough budget to spend.
Vendors can be extremely helpful when it comes to data privacy and the correct handling of sensitive data. One of these types of use cases involves a hospital or healthcare-related business that wants to utilize the power of machine learning without jeopardizing its compliance with HIPAA and other data privacy rules. Even outside the medical field, laws like the European GDPR are tightening control of data sets, and requiring more vigilance on the part of corporate stakeholders.
Data annotation requires skilled manpower to work on regardless of the size, scale and domain of your business. Even if you’re generating bare minimum data every single day, you need data experts to work on your data for labeling. So, now, you need to realize if you have the required manpower in place.If you do, are they skilled at the required tools and techniques or do they need upskilling? If they need upskilling, do you have the budget to train them in the first place?
Moreover, the best data annotation and data labeling programs take a number of subject matter or domain experts and segment them according to demographics like age, gender and area of expertise – or often in terms of the localized languages they’ll be working with. That’s, again, where we at Shaip talk about getting the right people in the right seats thereby driving the right human-in-the-loop processes that will lead your programmatic efforts to success.
Small and Large Project Operations and Cost Thresholds
In many cases, vendor support can be more of an option for a smaller project, or for smaller project phases. When the costs are controllable, the company can benefit from outsourcing to make data annotation or data labeling projects more efficient.
Companies can also look at important thresholds – where many vendors tie cost to the amount of data consumed or other resource benchmarks. For example, let’s say that a company has signed up with a vendor for doing the tedious data entry required for setting up test sets.
There may be a hidden threshold in the agreement where, for example, the business partner has to take out another block of AWS data storage, or some other service component from Amazon Web Services, or some other third-party vendor. They pass that on to the customer in the form of higher costs, and it puts the price tag out of the customer’s reach.
In these cases, metering the services that you get from vendors helps to keep the project affordable. Having the right scope in place will ensure that project costs do not exceed what is reasonable or feasible for the firm in question.
Open Source and Freeware Alternatives
Some alternatives to full vendor support involve using open-source software, or even freeware, to undertake data annotation or labeling projects. Here there’s a kind of middle ground where companies don’t create everything from scratch, but also avoid relying too heavily on commercial vendors.
The do-it-yourself mentality of open source is itself kind of a compromise – engineers and internal people can take advantage of the open-source community, where decentralized user bases offer their own kinds of grassroots support. It won’t be like what you get from a vendor – you won’t get 24/7 easy assistance or answers to questions without doing internal research – but the price tag is lower.
So, the big question – When Should You Buy A Data Annotation Tool:
As with many kinds of high-tech projects, this type of analysis – when to build and when to buy – requires dedicated thought and consideration of how these projects are sourced and managed. The challenges most companies face related to AI/ML projects when considering the “build” option is it’s not just about the building and development portions of the project. There is often an enormous learning curve to even get to the point where true AI/ML development can occur. With new AI/ML teams and initiatives the number of “unknown unknowns” far outweigh the number of “known unknowns.”
To make things even simpler, consider the following aspects:
- when you work on massive volumes of data
- when you work on diverse varieties of data
- when the functionalities associated with your models or solutions could change or evolve in the future
- when you have a vague or generic use case
- when you need a clear idea on the expenses involved in deploying a data annotation tool
- and when you don’t have the right workforce or skilled experts to work on the tools and are looking for a minimal learning curve
If your responses were opposite to these scenarios, you should focus on building your tool.
Factors to consider while choosing the right Data Annotation Tool
If you’re reading this, these ideas sound exciting, and are definitely easier said than done. So how does one go about leveraging the plethora of already existing data annotationn tools out there? So, the next step involved is considering the factors associated with choosing the right data annotation tool.
Unlike a few years back, the market has evolved with tons of data annotation tools in practice today. Businesses have more options in choosing one based on their distinct needs. But every single tool comes with its own set of pros and cons. To make a wise decision, an objective route has to be taken apart from subjective requirements as well.
Let’s look at some of the crucial factors you should consider in the process.
Defining Your Use Case
To select the right data annotation tool, you need to define your use case. You should realize if your requirement involves text, image, video, audio or a mix of all data types. There are standalone tools you could buy and there are holistic tools that allow you to execute diverse actions on data sets.
The tools today are intuitive and offer you options in terms of storage facilities (network, local or cloud), annotation techniques (audio, image, 3D and more) and a host of other aspects. You could choose a tool based on your specific requirements.
Establishing Quality Control Standards
This is a crucial factor to consider as the purpose and efficiency of your AI models are dependent on the quality standards you establish. Like an audit, you need to perform quality checks of the data you feed and the results obtained to understand if your models are being trained the right way and for the right purposes. However, the question is how do you intend to establish quality standards?
As with many different kinds of jobs, many people can do a data annotation and tagging but they do it with various degrees of success. When you ask for a service, you don’t automatically verify the level of quality control. That’s why results vary.
So, do you want to deploy a consensus model, where annotators offer feedback on quality and corrective measures are taken instantly? Or, do you prefer sample review, gold standards or intersection over union models?
The best buying plan will ensure the quality control is in place from the very beginning by setting standards before any final contract is agreed on. When establishing this, you shouldn’t overlook error margins as well. Manual intervention cannot be completely avoided as systems are bound to produce errors at up 3% rates. This does take work up front, but it’s worth it.
Who Will Annotate Your Data?
The next major factor relies on who annotates your data. Do you intend to have an in-house team or would you rather get it outsourced? If you’re outsourcing, there are legalities and compliance measures you need to consider because of the privacy and confidentiality concerns associated with data. And if you have an in-house team, how efficient are they at learning a new tool? What is your time-to-market with your product or service? Do you have the right quality metrics and teams to approve the results?
The Vendor Vs. Partner Debate
Data annotation is a collaborative process. It involves dependencies and intricacies like interoperability. This means that certain teams are always working in tandem with each other and one of the teams could be your vendor. That’s why the vendor or partner you select is as important as the tool you use for data labeling.
With this factor, aspects like the ability to keep your data and intentions confidential, intention to accept and work on feedback, being proactive in terms of data requisitions, flexibility in operations and more should be considered before you shake hands with a vendor or a partner. We have included flexibility because data annotation requirements are not always linear or static. They might change in the future as you scale your business further. If you’re currently dealing with only text-based data, you might want to annotate audio or video data as you scale and your support should be ready to expand their horizons with you.
One of the ways to assess vendor involvement is the support you will receive.
Any buying plan has to have some consideration of this component. What will support look like on the ground? Who will the stakeholders and point people be on both sides of the equation?
There are also concrete tasks that have to spell out what the vendor’s involvement is (or will be). For a data annotation or data labeling project in particular, will the vendor be actively providing the raw data, or not? Who will act as subject matter experts, and who will employ them either as employees or independent contractors?
Key Use Cases
Why do companies undertake these kinds of data annotation and data labeling projects?
Use cases abound, but some of the common ones illustrate how these systems help companies to accomplish goals and objectives.
For example, some use cases involve trying to train digital assistants or interactive voice response systems. Really, the same types of resources can be helpful in any situation where an artificial intelligence entity interacts with a human being. The more data annotation and data labeling have contributed to targeted test data, and training data, the better these relationships work, in general.
Another key use case for data annotation and data labeling is in developing industry-specific AI. You might call some of these types of projects “research-oriented” AI, where others are more operational or procedural. Healthcare is a major vertical for this data-intensive effort. With that in mind, though, other industries like finance, hospitalities, manufacturing or even retail will also use these types of systems.
Other use cases are more specific in nature. Take facial recognition as an image processing system. The same data annotation and data labeling helps to provide the computer systems with the information that they need to identify individuals and produce targeted results.
The aversion of some companies to the facial recognition sector is an example of how that works. When the technology is insufficiently controlled, it leads to vast concerns about fairness and its impact on human communities.
Here are some specific case study examples that address how data annotation and data labeling really work on the ground. At Shaip, we take care to provide the highest levels of quality and superior results in data annotation and data labeling.
Much of the above discussion of standard achievements for data annotation and data labeling reveals how we approach each project, and what we offer to the companies and stakeholders we work with.
Case study materials that will demonstrate how this works:
In a clinical data licensing project, the Shaip team processed over 6,000 hours of audio, removing all protected health information (PHI), and leaving HIPAA-compliant content for healthcare speech recognition models to work on.
In this type of case, it’s the criteria and classifying achievements that are important. The raw data is in the form of audio, and there’s the need to de-identify parties. For example, in using NER analysis, the dual goal is to de-identify and annotate the content.
Another case study involves an in-depth conversational AI training data project that we completed with 3,000 linguists working over a 14-week period. This led to the production of training data in 27 languages, in order to evolve multilingual digital assistants able to handle human interactions in a broad selection of native languages.
In this particular case study, the need to get the right person in the right chair was evident. The large numbers of subject matter experts and content input operators meant there was a need for organization and procedural streamlining to get the project done on a particular timeline. Our team was able to beat the industry standard by a wide margin, through optimizing the collection of data and subsequent processes.
Other types of case studies involve things like bot training and text annotation for machine learning. Again, in a text format, it’s still important to treat identified parties according to privacy laws, and to sort through the raw data to get the targeted results.
In other words, in working across multiple data types and formats, Shaip has demonstrated the same vital success by applying the same methods and principles to both raw data and data licensing business scenarios.
We honestly believe this guide was resourceful to you and that you have most of your questions answered. However, if you’re still not convinced about a reliable vendor, look no further.
We, at Shaip, are a premier data annotation company. We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to table competencies like commitment, confidentiality, flexibility and ownership to each project or collaboration.
So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.
Frequently Asked Questions (FAQ)
Data Annotation or Data Labeling is the process that makes data with specific objects recognizable by machines so as to predict the outcome. Tagging, transcribing or processing objects within textual, image, scans, etc. enable algorithms to interpret the labeled data and get trained to solve real business cases on its own without human intervention.
In machine learning (both supervised or unsupervised), labeled or annotated data is tagging, transcribing or processing the features you want your machine learning models to understand and recognize so as to solve real world challenges.
A data annotator is a person who works tirelessly to enrich the data so as to make it recognizable by machines. It may involve one or all of the following steps (subject to the use case in hand and the requirement): Data Cleaning, Data Transcribing, Data Labeling or Data Annotation, QA etc.
Tools or platforms (cloud-based or on-premise) that are used to label or annotate high-quality data (such as text, audio, image, video) with metadata for machine learning are called data annotation tools.
Tools or platforms (cloud-based or on-premise) that are used to label or annotate moving images frame-by-frame from a video to build high-quality training data for machine learning.
Tools or platforms (cloud-based or on-premise) that are used to label or annotate text from reviews, newspapers, doctor’s prescription, electronic health records, balance sheets, etc. to build high-quality training data for machine learning. This process also can be called labeling, tagging, transcribing, or processing.