A Beginner’s Guide to Data Annotation: Tips and Best Practices
The Ultimate Buyers Guide 2023
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 a few of the challenging aspects of your project. The output of your AI & ML models is only as good as the data you use to train them – 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.
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.
What is Data Annotation?
Data annotation is the process of attributing, tagging, or labeling data to help machine learning algorithms understand and classify the information they process. This process is essential for training AI models, enabling them to accurately comprehend various data types, such as images, audio files, video footage, or text.
Imagine a self-driving car that relies on data from computer vision, natural language processing (NLP), and sensors to make accurate driving decisions. To help the car’s AI model differentiate between obstacles like other vehicles, pedestrians, animals, or roadblocks, the data it receives must be labeled or annotated.
In supervised learning, data annotation is especially crucial, as the more labeled data fed to the model, the faster it learns to function autonomously. Annotated data allows AI models to be deployed in various applications like chatbots, speech recognition, and automation, resulting in optimal performance and reliable outcomes.
Importance of data annotation in machine learning
Machine learning involves computer systems improving their performance by learning from data, much like humans learn from experience. Data annotation, or labeling, is crucial in this process, as it helps train algorithms to recognize patterns and make accurate predictions.
In machine learning, neural networks consist of digital neurons organized in layers. These networks process information similar to the human brain. Labeled data is vital for supervised learning, a common approach in machine learning where algorithms learn from labeled examples.
Training and testing datasets with labeled data enable machine learning models to efficiently interpret and sort incoming data. We can provide high-quality annotated data to help algorithms learn autonomously and prioritize results with minimal human intervention.
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.
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.
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.
Image Classification – Image classification involves assigning predefined categories or labels to images based on their content. This type of annotation is used to train AI models to recognize and categorize images automatically.
Object Recognition/Detection – Object recognition, or object detection, is the process of identifying and labeling specific objects within an image. This type of annotation is used to train AI models to locate and recognize objects in real-world images or videos.
Segmentation – Image segmentation involves dividing an image into multiple segments or regions, each corresponding to a specific object or area of interest. This type of annotation is used to train AI models to analyze images at a pixel level, enabling more accurate object recognition and scene understanding.
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.
Sentiment annotation – Sentiment annotation involves labeling textual data with the sentiment it conveys, such as positive, negative, or neutral. This type of annotation is commonly used in sentiment analysis, where AI models are trained to understand and evaluate the emotions expressed in text.
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.
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.
Key Steps in Data Labeling and Data Annotation Process
The data annotation process involves a series of well-defined steps to ensure high-quality and accurate data labeling for machine learning applications. These steps cover every aspect of the process, from data collection to exporting the annotated data for further use.
Here’s how data annotation takes place:
- Data Collection: The first step in the data annotation process is to gather all the relevant data, such as images, videos, audio recordings, or text data, in a centralized location.
- Data Preprocessing: Standardize and enhance the collected data by deskewing images, formatting text, or transcribing video content. Preprocessing ensures the data is ready for annotation.
- Select the Right Vendor or Tool: Choose an appropriate data annotation tool or vendor based on your project’s requirements. Options include platforms like Nanonets for data annotation, V7 for image annotation, Appen for video annotation, and Nanonets for document annotation.
- Annotation Guidelines: Establish clear guidelines for annotators or annotation tools to ensure consistency and accuracy throughout the process.
- Annotation: Label and tag the data using human annotators or data annotation software, following the established guidelines.
- Quality Assurance (QA): Review the annotated data to ensure accuracy and consistency. Employ multiple blind annotations, if necessary, to verify the quality of the results.
- Data Export: After completing the data annotation, export the data in the required format. Platforms like Nanonets enable seamless data export to various business software applications.
The entire data annotation process can range from a few days to several weeks, depending on the project’s size, complexity, and available resources.
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.
What are the Benefits of Data Annotation?
Data annotation is crucial to optimizing machine learning systems and delivering improved user experiences. Here are some key benefits of data annotation:
- Improved Training Efficiency: Data labeling helps machine learning models be better trained, enhancing overall efficiency and producing more accurate outcomes.
- Increased Precision: Accurately annotated data ensures that algorithms can adapt and learn effectively, resulting in higher levels of precision in future tasks.
- Reduced Human Intervention: Advanced data annotation tools significantly decrease the need for manual intervention, streamlining processes and reducing associated costs.
Thus, data annotation contributes to more efficient and precise machine learning systems while minimizing the costs and manual effort traditionally required to train AI models.
Key Challenges in Data Annotation for AI Success
Data annotation plays a critical role in the development and accuracy of AI and machine learning models. However, the process comes with its own set of challenges:
- Cost of annotating data: Data annotation can be performed manually or automatically. Manual annotation requires significant effort, time, and resources, which can lead to increased costs. Maintaining the quality of the data throughout the process also contributes to these expenses.
- Accuracy of annotation: Human errors during the annotation process can result in poor data quality, directly affecting the performance and predictions of AI/ML models. A study by Gartner highlights that poor data quality costs companies up to 15% of their revenue.
- Scalability: As the volume of data increases, the annotation process can become more complex and time-consuming. Scaling data annotation while maintaining quality and efficiency is challenging for many organizations.
- Data privacy and security: Annotating sensitive data, such as personal information, medical records, or financial data, raises concerns about privacy and security. Ensuring that the annotation process complies with relevant data protection regulations and ethical guidelines is crucial to avoiding legal and reputational risks.
- Managing diverse data types: Handling various data types like text, images, audio, and video can be challenging, especially when they require different annotation techniques and expertise. Coordinating and managing the annotation process across these data types can be complex and resource-intensive.
Organizations can understand and address these challenges to overcome the obstacles associated with data annotation and improve the efficiency and effectiveness of their AI and machine learning projects.
What is Data Labeling? Everything a Beginner Needs to Know
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.
How to Choose The Right Data Annotation Tool for Your Project
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?
Real-World Use Cases for Data Annotation in AI
Data annotation is vital in various industries, enabling them to develop more accurate and efficient AI and machine learning models. Here are some industry-specific use cases for data annotation:
What are the best practices for data annotation?
To ensure the success of your AI and machine learning projects, it’s essential to follow best practices for data annotation. These practices can help enhance the accuracy and consistency of your annotated data:
- Choose the appropriate data structure: Create data labels that are specific enough to be useful but general enough to capture all possible variations in data sets.
- Provide clear instructions: Develop detailed, easy-to-understand data annotation guidelines and best practices to ensure data consistency and accuracy across different annotators.
- Optimize the annotation workload: Since annotation can be costly, consider more affordable alternatives, such as working with data collection services that offer pre-labeled datasets.
- Collect more data when necessary: To prevent the quality of machine learning models from suffering, collaborate with data collection companies to gather more data if required.
- Outsource or crowdsource: When data annotation requirements become too large and time-consuming for internal resources, consider outsourcing or crowdsourcing.
- Combine human and machine efforts: Use a human-in-the-loop approach with data annotation software to help human annotators focus on the most challenging cases and increase the diversity of the training data set.
- Prioritize quality: Regularly test your data annotations for quality assurance purposes. Encourage multiple annotators to review each other’s work for accuracy and consistency in labeling datasets.
- Ensure compliance: When annotating sensitive data sets, such as images containing people or health records, consider privacy and ethical issues carefully. Non-compliance with local rules can damage your company’s reputation.
Adhering to these data annotation best practices can help you guarantee that your data sets are accurately labeled, accessible to data scientists, and ready to fuel your data-driven projects.
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.