Data Annotation Services

We provide data annotation and labeling services to help deploy AI and Machine Learning models for diverse businesses.

  • Domain experts
  • Scalable workforce
  • Proven track record
Contact Us Now

High-Quality Training Data through Expert Data Annotation Services

In the world of AI and ML, the success of your algorithms hinges on the quality and consistency of your training data. Sourcing this data can be a challenge, especially with the need for the right annotation tools and technology to ensure a smooth workflow.

Hitech BPO, a leading data annotation company, tackles these challenges head-on with our extensive experience and a highly scalable workforce. Our skilled data annotators meticulously create high-quality training datasets from various data types, including text, images, videos, LIDAR point cloud, and more – structured and unstructured alike. We leverage our expertise with advanced labeling tools like YOLO, SSD, and RetinaNet to accelerate the data annotation and labeling process.

Our comprehensive data annotation services encompass a wide range of solutions, including semantic annotation, text categorization, bounding box annotation, 3D point cloud annotation, named entity recognition, and more. This allows us to cater to diverse project needs across various industries. Additionally, our scalable workforce ensures cost-effective handling of even the most voluminous data annotation requirements.

Outsource your data annotation today!  →
What our Customer Says

Hitech BPO not only annotated data, we also got a dedicated team that was knowledgeable and confident to exchange competencies and creative solution-oriented approaches, which definitely fast tracked our work.

Vice President AI-Technologies for automated driving

Case Study

Discover our Data Annotation Works

YouTube video

Accurate Image Annotation for Swiss Food Waste Assessment Solutions

Accurately annotate and label all interest areas in kitchen waste images and food images for training machine learning models

View Case Study »

Text Classification for Germany-Based Construction Technology Company

Comprehending and extracting relevant information from construction articles and labeling the information based on categories.

View Case Study »

Live traffic video stream annotation for Data Analytics Company

The client needed categorize and label the huge number of vehicles based on movement to develop machine learning solutions to predict traffic-related issues.

View Case Study »

Our core data annotation & labeling offerings

Data Annotation Solutions

Computer Vision

Computer vision solutions interpret images and videos, enabling automated object detection and analysis. Employ annotation services that transform visual data into actionable insights and optimize industrial operations.

Learn More »

NLP Solutions

NLP annotation solutions understand, process, and generate sentiment analysis. They also power customer support automation and content categorization in textual data for strategic decision-making.

Learn More »
Benefits

Why choose Hitech BPO for data annotation

Scalable Solutions

Scalable Solutions

we can easily expand our team and upgrade technologies as per work nature and volume providing scalable solutions.

Quality Deliverables

Quality Deliverables

Well placed AI audit system, built-in validation, multi-level quality control and performance evaluation ensure 99.5% accuracy.

Data Security

Data Security

100% data security is assured where data is protected at every stage no matter what device or technology is used to process the data.

24/7 Service

24/7 Service

With global clients we make sure that our team is available 24/7.

Industries and Verticals, We Serve

Our versatile data annotation services are tailored for various industries and domains.

Data Annotation FAQs

 

Why is data annotation and labeling important in machine learning?

Data annotation and labeling play a crucial role in machine learning because they serve as the foundation for training and evaluating machine learning models. This includes assigning meaningful information or tags to raw data, such as images, text, audio, or video, to create a structured dataset that can be used by algorithms to learn patterns and make predictions. Data annotation and labeling enable supervised learning, improve model performance and interpretability, facilitate transfer learning and active learning, and provide a basis for evaluation and benchmarking. High-quality, accurately labeled data is essential for developing robust and reliable machine learning models that can be deployed in real-world applications.

What types of data can be annotated and labeled?

Data annotation and labeling can be applied to various types of data, depending on the specific requirements of a machine learning task. Here are some common types of data that can be annotated and labeled:
  • Text data: Text data can be annotated at different levels, such as character, word, sentence, or document level.
  • Image data: Image data annotation involves labeling objects, regions, or attributes within images.
  • Audio data: Audio data annotation involves labeling various aspects of audio signals, such as speech, music, or environmental sounds.
  • Video data: Video data annotation combines aspects of image and audio annotation, as well as temporal information.
  • Time-series data: Time-series data annotation involves labeling sequences of data points collected over time, from domains like finance, healthcare, and IoT.

What annotation techniques does Hitech BPO employ?

As a top-notch data annotation service provider, Hitech BPO employs a variety of techniques to ensure high-quality, accurate, and consistent annotations. Here are the key data annotation techniques and best practices that we follow:
  • Manual annotation: Well-trained and knowledgeable human annotators follow clear guidelines and instructions to ensure uniformity across training datasets.
  • Quality assurance: A robust quality assurance process is an integral part of the data annotation process to maintain high annotation quality.
  • Automation and semi-automation: Leveraging machine learning models and other automation tools to speed up the annotation process and reduce manual effort. We also use pre-trained models to generate initial annotations, which are then reviewed and refined by human annotators.
  • Active learning: we incorporate active learning techniques to optimize the annotation process by selecting the most informative samples for our human annotators. It fast tracks model convergence.
  • Ontologies and taxonomies: Develop well-defined ontologies and taxonomies to ensure consistency and structure in the annotated data. These provide a common vocabulary and framework for annotators to follow, reducing ambiguity and confusion.
  • Scalability and flexibility: We are equipped at using cloud-based infrastructure, parallel processing, and other techniques, to scale our operations to handle large volumes of data.
By employing these techniques and best practices, we ensure high-quality, accurate, and consistent annotations that contribute to the success of machine learning projects.

Can Hitech BPO handle large-scale annotation projects?

Yes. Hitech BPO is equipped to handle large-scale annotation projects. We employ a combination of strategies, tools, and processes to effectively handle large-scale annotation projects.
  • Project management: This involves setting clear objectives, timelines, milestones, and deliverables, as well as assigning roles and responsibilities to team members.
  • Workforce management: We hire and train annotators with the necessary domain expertise. We implement a shift-based work schedule to maximize productivity and coverage.
  • Scalable infrastructure: We leverage cloud-based infrastructure and parallel processing techniques to scale the annotation process to handle large volumes of data. This helps us to allocate resources dynamically based on project requirements and workload.
By adopting these strategies and best practices, Hitech BPO effectively handles large-scale annotation projects, delivering high-quality annotated data that contributes to the success of machine learning applications.

How does Hitech BPO ensure data quality and accuracy in annotation?

Hitech BPO has an established and robust quality assurance process for maintaining high annotation quality in large-scale projects. This includes:
  • Multiple annotators working on the same data to reduce individual biases and errors.
  • Regular reviews and feedback sessions to address any issues or discrepancies.
  • Cross-validation, where a subset of the data is annotated by multiple annotators and their work is compared to assess agreement and consistency.

How does Hitech BPO handle sensitive or confidential data during annotation?

Hitech BPO is aware of how important it is to ensure data privacy and security in large-scale projects involving sensitive or personally identifiable information (PII). Implementing strict access controls, encryption, and other security measures help us protect the data and maintain compliance with relevant regulations.

Can Hitech BPO handle customized annotation requirements?

We at Hitech BPO are adept at handling customized annotation requirements by employing a flexible and adaptive approach to meet the specific needs of their clients. We resort to client consultation, adhere to custom guidelines, leverage flexible workflows, use custom tools and platforms and much more.

How can businesses get started with Hitech BPO’s data annotation and labeling services?

To get started with Hitech BPO’s data annotation and labeling services, businesses can follow these steps:
  • Define project objectives: Clearly outline the goals and objectives of the project, including the type of data to be annotated, the desired output format, and the specific machine learning tasks the annotated data will be used for.
  • Request proposals: Reach out to Hitech BPO’s representative request proposals or quotes for your project. Provide them with detailed information about your requirements, including data volume, annotation types, project timeline, and any specific customization needs.
  • Evaluate proposals: Carefully evaluate our proposal based on factors such as cost, turnaround time, quality assurance processes, scalability, and the provider’s ability to handle customized requirements. Don’t forget to ask for our case studies about domain experience in similar projects.
  • Develop annotation guidelines: Collaborate with our project managers to develop clear and comprehensive annotation guidelines and instructions. These should cover all relevant annotation rules, examples, and any specific requirements for your project.
By following these steps, businesses can effectively engage with Hitech BPO’s data annotation and labeling services. We ensure high-quality annotations that contribute to the success of your machine learning projects.

Let Us Help You Overcome
Business Data Challenges

What’s next? Message us a brief description of your project.
Our experts will review and get back to you within one business day with free consultation for successful implementation.

image

Disclaimer:  

HitechDigital Solutions LLP and Hitech BPO will never ask for money or commission to offer jobs or projects. In the event you are contacted by any person with job offer in our companies, please reach out to us at info@hitechbpo.com

popup close