Best Data Labeling and Annotation Services for AI & Machine Learning
Top-notch annotation service for machine learning and artificial intelligence enterprises that need high-quality training data for a wide range of industries.
SECURITY AND CONFIDENTIALITY
As a recognised and credible company, we ensure that our customers’ sensitive information is protected at all times when working with them.
WE OPERATE AT A SCALE
A scalable solution with quick turn-around time to satisfy the varying demands of many clients is provided by employing hundreds of people to annotate images as needed.
PERFORMANCE WITH QUALITY
Take use of high-quality, very accurate image annotation services that go through numerous rounds of auditing and analysing labelled data before they are delivered.
Outsourcing image annotation to us means that our clients receive a cost-effective data labelling solution that aids them in reducing project costs while also maximising productivity.
Gain most out of AI
Focus on growth and innovation
Multi-Source/ Cross-Industry capabilities
Stay ahead of the competition
Enhanced Data Security
Availability & Delivery
Do you use any particular software? Can I do my work on this platform? Are there features to keep track of and manage all my annotations?
For your ML/AI project, a professional data annotator will employ sophisticated labelling tools. Controlling your annotation crew should not be a problem if you choose one of the top software solutions, which include capabilities such as tracking, reporting, and quality assurance (QA).
What methods will I use to teach my new employees? How long will it take, and how much time will it take?
Experts in your field are typically assigned to annotate your data by data labelling service providers. Documentation and a few meetings are usually all it takes for a new task to get started in an AI project. In contrast, training for highly specialised fields such as healthcare or agriculture may take longer.
How can the quality of data labelled for machine learning be assessed? Is there an example review available on your site? What if this quality doesn’t meet my project’s needs?
There are a variety of metrics you may use to gauge the efficiency of your annotation crew. An organization’s productivity can be measured, for example, by the calibre, quantity, and level of commitment of its employees. In order to properly classify and evaluate errors, it is essential to conduct a sample review.
How will I keep in touch with my new coworkers? Will they be able to respond to my queries and provide me an answer?
Labeling team reaction times can be critical when it comes to your product or customer experience. That’s why it’s so important to establish up the correct communication channels and schedules between your machine learning and annotation teams.
Is the data you have on me safe? Is there a way to ensure that only those who are permitted can access it? Every data labeler needs to sign a Non-Disclosure Agreement (NDA).
One of the responsibilities of a reputable outsourcing company is to keep client data safe. Access-setting features in modern picture, audio, video, and text annotation applications aid in corporate data control. Top data labelling suppliers will also sign non-disclosure agreements to protect the privacy of their clients.
It’s important to know if current privacy laws will protect the sensitive information. Is the personal information in my raw dataset secure?
In order to comply with GDPR, CCPA, and other privacy laws, most advanced data annotation software includes capabilities that assist your firm keep the personal information in your dataset secret. No other tools or add-ons are required, so you don’t have to worry about compromising your privacy.
Do I have to pay a fee? What factors influence the pricing of a product?
Data volumes, the number of active labelers, and annotations all influence the cost of a project. Classification is the most affordable assignment, whereas object identification is the most expensive.
Is it possible that you’ll meet my deadlines? And what if the data labelers need more time to finish the project than originally planned? How many people should I have on my team?
As a manager of an annotating team, you should be able to predict the time and resources needed to label a given volume of data. Before the team begins working on your dataset, you should discuss and document any potential grounds for deadline re-scheduling in a formal contract..
What method will you use to achieve your goals? Does it matter what format I use?
A user should have the option of choosing the output data format. Any labelled data machine learning output doesn’t matter if it’s in Excel, XML, CSV, JSON, or any other extension type.
What if, in the middle of the project, I need to alter the volume or labelling approach? Will my team be able to adapt to new demands if they come my way?
To be really customer-focused, a service provider must be flexible enough to adapt to the ever-changing needs of its clients. So, there is the option to hire additional data labelers or devote more time to the project.