Annotation Services

Data annotation insights for machine learning and artificial intelligence

While artificial intelligence (AI) fosters intelligent human–computer interactions, machine learning (ML) enables machines to become smarter with every interaction and reduces human effort and time. But did you know that data annotation plays a vital role in the success of AI and ML projects? Data labelling helps identify objectives in raw data formats, and tagging with labels helps the ML model to handle all the projects accurately. So, in other words, data annotation enhances the function of ML.

But we're sure business owners expect a few other aspects regarding the role of data annotation in ML and AI to be covered. Hence, let's look at a few insights pertaining to data annotation for ML and AI.

Four things to know about data annotation in ML and AI

From the types of data annotation, technical aspects such as data annotation recommendations in ML to commercial queries concerning in-house or outsourced data annotation, there are a few factors that every company looking to start AI/ML projects must know about data annotation.*

Types of data annotation

Data annotation involves labelling content to make it recognisable to machines through computer vision or NLP-based AI or ML training in various formats. Annotation depends on the type of content involved. Accordingly, the types of annotation include:

Steps involved in data annotation

The first step is acquisition. It involves collecting and aggregating data and sourcing the subject matter expertise (SME), either from human operators or with the help of a data licensing contract. You can leverage various sources to collect the data you need for the annotation project. The second step involves actual labelling and annotation through various techniques such as semantic segmentation, bounding box annotation, NLP annotation, etc.

Data annotation recommendation for ML

For ML projects, experts suggest annotating raw data, as it is the best programming method for ML. It is also because coding for ML requires data, and annotating raw data provides an organised form of data to develop a program code.
How much data is needed for ML projects? It depends on the project. In some cases, you can establish limits based on the subject. For instance, population growth trends for the past 20 years. However, SMEs usually calculate the amount of data required and assess the accuracy to develop the 'ground truth' required to train ML algorithms.

Data annotation in ML/AI: In-house or outsourced?

Outsourced data annotation has proven to be a commercially and technically better as it helps save a lot of money compared with insourcing or in-house annotation. In fact, as per a report, in-house data annotation is likely to prove four to five times costlier than outsourcing, considering the infrastructure, expertise, and employment costs attached to it.

Furthermore, outsourcing refers to a better professional commitment and higher scalability. It also involves a higher degree of professional expertise, experience, and substantial and sustainable cost savings through ready infrastructure without bearing employment costs.

Expert annotation companies serving a global clientele follow international data security standards and regulatory frameworks such as HIPPA GDPR, etc., for enhanced data privacy and safety.

Besides, you can have a team working remotely, without requiring you, in particular, to make online working arrangements, as the service providers take care of it.

How can Infosys BPM help?

Infosys BPM empowers companies across various business domains with the best annotation services. The company's annotation capabilities include:

    Text annotation:

    Entity tagging, linking classification, and sentiment tagging

    Image annotation:

    Bounding box, segmentation, polygons, and classification

    Audio annotation:

    Transcription, grading, classification, and sentiment tagging

    Video annotation:

    Bounding box, segmentation, polygons, and key points

    Sensor data annotation:

    Pattern tagging, LiDAR time series tagging, and 3D point cloud

Additionally, the Infosys BPM value propositions include flexible and scalable managed services operating model, a robust QA framework that ensures over 98% accuracy, AI-assisted annotated tools for 30% faster annotation, and the capability to work on third-party annotation tools. The company aims to enhance AI/ML experiences and deliver better customer experiences through quicker and more accurate data annotation.
*For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive yet modular suite of services is doing exactly that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.

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