Brands that Trust LexiConn for Content Development
Don't Be Split Between Train & Test
Even with several zettabytes of data generated daily, 90% of it is unstructured, presenting challenges for data science professionals seeking structured and tagged data to train their models.
Data labeling is the fundamental starting point for any successful model training, involving precise labeling and tagging of data to create informative datasets. However, this process can be arduous, especially at scale, requiring expertise and resources.
To overcome these hurdles, outsourcing Data labeling emerges as a viable solution. By entrusting this task to specialized providers, data scientists can streamline their efforts, ensuring accurate annotations while focusing on refining models and extracting valuable insights from the data.
Outsourcing empowers data science professionals to leverage the full potential of data and drive AI innovation confidently.
Why LexiConn for Data Labeling
Attention to Detail
We pay close attention to the details of the data and ensure that the annotations are correct and consistent.
Domain Knowledge
Depending on the application, accuracy, and nature of the dataset, we offer domain knowledge to our annotators.
Consistency
We maintain consistency by developing predefined guidelines and standards to achieve scale.
Adaptability
Our annotators can adapt to handle different types of data and annotation tasks effectively.
Critical Thinking
Our team is empowered to tackle ambiguous or challenging situations and make informed decisions.
Ethical Awareness
We are aware of the ethical considerations and follow privacy to ensure data protection.
Project Management
We have the experience and the tools to coordinate efforts, resolve doubts, and ensure a cohesive annotation process.
Technical Proficiency
Our annotators are comfortable using multiple annotation software, tools and workflows required as per the use case.
Quality Control
We are open to feedback and participate in our client's quality control processes to continuously improve accuracy.
Data labeling is essential in training AI models for image and object recognition. Accurate labeling and tagging of images provide ground truth for algorithms to differentiate and identify objects.
It finds use in autonomous vehicles for detecting pedestrians, traffic signs, and obstacles. Facial recognition relies on Data labeling to verify individuals for security applications, and in surveillance systems for activity detection.
It finds use in autonomous vehicles for detecting pedestrians, traffic signs, and obstacles. Facial recognition relies on Data labeling to verify individuals for security applications, and in surveillance systems for activity detection.
Data labeling is pivotal in training NLP models to understand human language. Labeled text data enables sentiment analysis for gauging customer opinions.
Named Entity Recognition (NER) involves identifying entities like names and locations. Language translation benefits from annotated parallel corpora for accurate translations.
Named Entity Recognition (NER) involves identifying entities like names and locations. Language translation benefits from annotated parallel corpora for accurate translations.
Annotated audio data is vital in training speech recognition systems, enabling them to convert spoken language into accurate text transcriptions.
Data labeling involves aligning audio recordings with their corresponding transcriptions, allowing AI models to learn the mapping between speech signals and textual representations. Speech recognition is integral in enhancing accessibility for individuals with visual impairments.
Data labeling involves aligning audio recordings with their corresponding transcriptions, allowing AI models to learn the mapping between speech signals and textual representations. Speech recognition is integral in enhancing accessibility for individuals with visual impairments.
In the field of medical imaging, Data labeling is indispensable. Annotated medical images serve as labeled examples to train AI models for tasks like tumor detection, organ segmentation, and disease classification.
By accurately annotating areas of interest in medical images, annotators provide the necessary information for AI models to assist healthcare professionals in identifying and diagnosing various conditions.
By accurately annotating areas of interest in medical images, annotators provide the necessary information for AI models to assist healthcare professionals in identifying and diagnosing various conditions.
Data labeling is pivotal in training recommendation systems that provide personalized suggestions to users based on their preferences and behavior.
By annotating user interactions, such as clicks, purchases, and ratings, AI models can learn user preferences and patterns to recommend products, services, or content tailored to individual tastes.
Recommendation systems are also applied in content streaming services making it easier for users to discover new content aligned with their interests.
By annotating user interactions, such as clicks, purchases, and ratings, AI models can learn user preferences and patterns to recommend products, services, or content tailored to individual tastes.
Recommendation systems are also applied in content streaming services making it easier for users to discover new content aligned with their interests.
Looking for a Data Labeling Partner?
Talk to LexiConn. We can put together a dedicated team to deliver large scale Data labeling services for complex use cases.
Discuss a PilotSolutions to Top Six Pitfalls in Data labeling
Bias and Subjectivity
Provide comprehensive guidelines and training to promote objectivity and diversity among annotators.
Inconsistent Annotations
Establish detailed guidelines and encourage open communication with annotators to ensure consistency.
Scalability Challenges
Leverage annotation tools, automation, and skilled project management for efficient scaling.
Ethical Issues
Implement strict data privacy protocols, anonymization, and consent processes to address privacy concerns.
Annotation Errors
Conduct regular training, quality checks and validation by experts to rectify errors, and improve processes.
Domain Expertise
Involve domain experts in the annotation process for accurate and context-specific labeling .
Everything is Data, Data is Everything
Data is all around us, and annotation plays a critical role in making sense of this data. However, there are challenges. From addressing bias and subjectivity to ensuring consistency and scalability, Data labeling demands meticulous attention and expertise.
At LexiConn, we know a thing or two about overcoming these bottlenecks. In the past few years, we have supported India's leading OTT platform in tagging over 1.4 lakh videos, elevating their content recommendation system to new heights.
With our meticulous Data labeling services, we empower AI-driven companies to overcome obstacles and achieve success in artificial intelligence. Want to know more?
I have read and accept the Privacy Policy
Read a Free Sample