Top AI Solutions Start With Professional ML Labeling Services
Machine learning annotation is the process of placing labels on text, audio, video, or images. These manually-set tags make datasets understandable to machines. And the more correct such annotations are, the more reliable your AI-powered software will be. So ML models can be effective only with high-quality training data.
AI algorithms support many industries. Healthcare, IT, telecommunications, security, e-commerce, real estate, agriculture, and the automotive industry are just a few to mention. Chatbots, self-driving cars, diagnose-detecting tools, plant-growth monitoring software, inventory-management instruments, face recognition technologies, remote safety control, etc., use labeled data.
What Is ML Annotation?
Main Applications of ML Labeling
Data annotation is used in various domains to train machine learning models. Some of the main applications of ML labeling include natural language processing (NLP) for tasks such as sentiment analysis and named entity recognition, computer vision for object detection and image classification, and speech recognition for transcribing audio data.
Data Annotation Services We Provide
Our data labelers use advanced tools to perform various annotation tasks effectively and quickly. We assemble teams considering their expertise and ensure that they understand how to use labeling software required for your tasks. Here are the data labeling services we offer:
Text labeling
This service includes identifying intent, emotions, requests, orders, names, and other elements in text or classifying entire documents.
Image processing
Marking people, animals, buildings, faces, and road marks with various techniques is what our labelers do effectively.
Speech recognition
We can teach your algorithms to understand speech patterns and styles, identify languages and dialects, and even detect accents.
Video annotation
Our data annotators process videos frame by frame with no compression. So you get the processed file in the initial quality.
NLP Annotation Types
Natural language processing (NLP) tags help machines understand people, perform required actions, and generate answers. Natural language annotation for machine learning includes the following types of labeling:
Entity annotation
This technique marks certain blocks of information like proper names, keywords, or parts of speech. So spam filters, search engines, chatbots, and translating tools use these datasets.
Entity linking
This is the next stage of text annotation machine learning models use. Annotators link labeled elements to databases containing relevant information. So entity linking assumes that annotators add URLs to words or phrases.
Text classification
Using this approach, data labelers tag entire sentences, paragraphs, or texts. This way, machines learn to categorize documents, classify products, or mark emotions, improving user experience and search results.
Sentiment analysis
This is one of the critical NLP annotation services for business-aimed software developers. It allows programs to understand the emotions behind the words. This way, AI can detect the appeal in product reviews or social media posts.
Linguistic annotation
This category has a variety of language data that specialists can mark. It includes word definitions, intonations, pauses, parts of speech, and other grammatical elements.
Computer Vision Annotation Types
While text annotation for machine learning models helps your AI projects hear and read texts, computer vision annotation lets machines see like humans. Below are the fundamental approaches that help computers do that:
Bounding boxes
With these rectangular boxes, data labelers identify 2D objects in images and videos.
Polygonal segmentation
When you need to detect objects precisely, data annotators use complex polygons.
Semantic segmentation
Assigning every pixel on an image to a specific class, leaving no white space, is called semantic segmentation.
3D cuboids
This annotation type adds volume to objects so that algorithms can analyze their position compared to other objects.
Key-point and landmark recognition
With points and lines, data labelers create the skeleton of an object. Such datasets are used for face recognition algorithms.
Lines and splines
Identifying road markings, sidewalks, roadsides, lanes, and similar objects lines and spines is critical for self-driving tools.
How to Get Machine Learning Labeling Services With Us
Once you decide to work with our company, check how we do it. You’ll need to:
1. Fill in the form below
You'll have to leave your contact details and mention how many people you want to hire and for how long. It'll take our managers an hour or two to process your request and return to you with a call.
2. Share your requirements
We'll ask you for more details of your project and discuss the work volume, deadlines, and budgets. Our specialists will advise you on the most suitable cooperation model and recommend the cost-effective option.
3. Consider the estimated budget
Our team will forward you the preliminary calculations as soon as possible. They'll include salaries and local employment taxes, expenses on office premises and utilities, and costs for the equipment and software needed.
4. Pay the invoice and start collaboration
Once we agree on all the candidates and run interviews, we will build your dedicated data labeling team. Then we'll finalize all the costs and send you one invoice. As soon as you pay it, our managers start the collaboration.
Top 6 Benefits to Outsource ML Annotation to Us
Labeling data for machine learning projects with our specialists has several advantages:
You cut labor costs
We work with professionals employed in Ukraine. And since the cost of living in this country is lower than in the US or Europe, payrolls are also lower. For you, this means spending 15-20% less on ML data labeling.
You stay focused on the project
Our cooperation format assumes that we take on all the administrative issues. This way, you don't have to think about finding office premises, paying utilities, or transferring taxes on salaries.
Your team is in the comfortable time zone
If you're located in a European country, you'll have a maximum of two hours of time difference with your labeling team. The US customers will still be able to contact their teams before the night hours.
You leverage advanced technology
We equip your dedicated ML labeling specialists with top-notch tools. That's why with our data annotation machine learning services, you'll invest in software that ideally matches your requirements for tag types.
You can launch the project quickly
Our recruiters create pools of candidates ready to start working asap, so you can start labeling machine learning datasets soon. We can interview, contract, and onboard your candidates in a couple of weeks.
You can scale anytime
Often, customers need more data annotation services for machine learning projects. But finding several more well-trained labeling specialists can take weeks. Our outsourcing format assumes you get the solution quicker.
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Managed document labeling service
If you manage a big data project or have to generate large-volume datasets, get a dedicated team. Rest assured that these professionals will follow your guidelines and have strong communication skills. They’ll be flexible, reliable, and responsive. Whether it’s placing bounding boxes or text annotation services you need, our experts will be ready to help.
03
Full-time labeling specialist dedicated to your company
Once you have larger work volumes that one specialist can still cover, hire a full-time data labeler. This option is also great if you have several smaller projects. For instance, if you need NLP text annotation and semantic segmentation, we can find one full-time worker instead of two part-time ones. However, we’ll have to consider the industry, annotation type, and workload.
3 Cooperation Models We Offer
Pick up the most suitable model, depending on the volume of data annotation services for machine learning projects you run. We offer:
02
Part-time data annotator dedicated to your project
We work only with trusted candidates and assign them mainly for long-term projects. But when you only kickstart your AI project, you may need limited labeling services for the machine learning models you train. That’s when a part-time data annotator can cover your tasks. This person will be dedicated to your project and available during the agreed working hours.