Data Annotation

To let no obstacle hurdle your way to quality data

What is Data Annotation?

 What if you need to create just an AI model? You just need data!

 What if you need to create an amazing AI model? You need a lot of quality data!

Data annotation hence, is the process of adding quality to the random, unsorted and scattered data in the form of text , audio ,image or video through the labeling.

Data annotation is the process of adding quality to the random, unsorted and scattered data in the form of text , audio ,image or video through the labeling.​

Why is Data Annotation
Important For You?

Quality data is indeed the new oil but equally true about the oil is very scare and difficult to find. Although the potential is huge and the oil can bring a lot of advantages and value to the enterprises across the globe, all you need to do is to tap it.

What if you didn't get your data annotated?

 Machine learning algorithms require large amounts of data before they begin to give useful results.

 Neural networks are data-eating machines that requires copious amounts of training data.

 The larger the architecture, the more data is needed to produce viable results.

One of the most important aspects of the AI model development is data. Therefore, it becomes very necessary for the organizations to gather and extract enough data from wherever they can to feed their machine learning models and thus make them better and efficient. More is the quality data fed to the model; more is the ability of the model to perform and higher becomes the efficiency of the model.

There might be an abundance of data around but it is usually raw and cannot be put into use to create state of the art AI models. Thus, the raw data has to be collected, transformed, analyzed, and experimented to be fed to the machine learning models for them to work with effectiveness and efficiency.

Our Process Of Data Annotation

One of the most important aspects of the AI model development is data. Therefore, it becomes very necessary for the organizations to gather and extract enough data from wherever they can to feed their machine learning models and thus make them better and efficient. More is the quality data fed to the model; more is the ability of the model to perform and higher becomes the efficiency of the model.

There might be an abundance of data around but it is usually raw and cannot be put into use to create state of the art AI models. Thus, the raw data has to be collected, transformed, analyzed, and experimented to be fed to the machine learning models for them to work with effectiveness and efficiency.

Use Cases!

Dataset Stories!

Legal Clause Classification

We often see hundreds of contracts or tenders coming up for review. In law firms we usually come across many tenders and their described contents and the first thing we do is to identify from which clause it comes from. So to build a clause identification model we need to have data, which needs to be annotated according to the required clauses. 

Resume NER

To avoid the delays and enhance the efficiency in the HR field, we need to automate this process to focus on more important sections in less time. How can we automate the resume screening process? By using Natural Language Processing techniques, we can create a model/system to automate the process. If we want to build an accurate and effective model, we need a proper Resume Named Entity Recognition annotated dataset.

Intent Classification Dataset

The intent classification dataset is used for classifying the intention of a text/ sentence. In the intent classification dataset, every text/sentence of the dataset is associated with one or more corresponding intent labels. This intent Classification Dataset has different kinds of sentences such as questions, suggestions, feedback, etc. about different products.

Car Brand/Model Detection Dataset

Car brand and model detection is a computer vision based model/system that can provide incredible value while doing car monitoring/tracking and detection. Nowadays many industries use this kind of vehicle brand/ model detection such as transportation, security, marketing and law enforcement, etc. Car brand and model detection is an important part in many real-time applications for example automatic vehicle surveillance, traffic management, driver assistance systems, traffic behavior analysis, and traffic monitoring, etc.

Types Of Data Annotation

Image classification involves teaching an Artificial Intelligence (AI) how to detect objects in an image based on their unique properties. An example of image classification is an AI that detects how likely an object in an image is to be an apple, orange or pear.

 

 

Using the bounding box to easily move, rotate, duplicate, and scale objects by dragging the object or a handle (one of the hollow squares along the bounding box).

 

Annotate all shapes and sizes. Label and Detect Objects. Accurately define lanes. 2D annotation shapes including complex polygons, bounding boxes, points, and cuboids onto images. Trace and annotate lines for lane line labeling.

 

Semantic segmentation involves the process where all objects of the same type are marked using one class label while in instance segmentation similar objects get their own separate labels.

 

Landmark annotation works by placing points across an image to label objects within that image. This kind of labeling ranges from single points to annotate small objects, and also multiple points to outline particular details. Images for landmark annotation can include maps, faces, bodies, and objects.

 

Optical character recognition (OCR) is a technology that allows converting static documents, such as physical forms, into a format that’s searchable and editable.

 

Document classification refers to a process of assigning one or more labels for a document from a predefined set of labels. The main issues in document classification are connected to classification of free text giving document content.

 

Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish). Human and machine translation each have their share of challenges.

 

Named entity recognition (NER) helps you easily identify the key elements in a text, like names of people, places, brands, monetary values, and more. Extracting the main entities in a text helps sort unstructured data and detect important information, which is crucial if you have to deal with large datasets.

 

Relationships are the grammatical and semantic connections between two entities in a piece of text. Predictly uses a combination of deep learning and semantic rules to recognize and extract the action that connects entities: their relationship.

 

Dependency parsing aims to predict these dependency relations between lexical units to retrieve information, mostly in the form of semantic interpretation. It analyzes the grammatical structure of a sentence, establishing relationships between “head” words and words which modify those heads.

 

Question answering is a computer science discipline within the fields of information retrieval and natural language processing, which is concerned with building systems that automatically answer questions posed by humans in a natural language.

 

Predictly's Unique Features!

Our technology stack has the cutting edge libraries/frameworks, like Docker and Kubernetes, that empower us to enable Machine Learning at production level.

Data Management is a process which includes gathering, validating, storing, protecting, and processing of the required data to ensure the accessibility, reliability, and timeliness of the data. Organizations and enterprises make use of this Big Data to make critical business decisions and gain deep insights into customer behavior, trends, and opportunities for creating extraordinary customer experiences. At Predictly, we manage the data, parse it from an unstructured form to a structured form, and divide it into smaller chunks to work on it effectively, without compromising the quality of data.

A manual annotation process not only is highly time-consuming but also lacks cost- effectiveness. That’s what makes ML Enabled Annotation at Predictly one of the very helpful tools which work in a semi-automatic way of labeling data with the help of machine learning. By using Active learning we make the process into a human-machine loop which starts with minimum data proposed by the probabilistic model and goes on to train and predict the new batch of data and keeps improving the trained model which makes the process faster and cost-effective.

One of the most trustable and well-accepted processes of annotation as well as the quality assurance of the data is manual labeling and manual quality check of the data. At Predictly, the data is annotated by a team of skilled and professional data annotators of the specific domain over a highly secure and featured annotation tool. Once the data is annotated, the data is passed on to the quality check team which comprises the highly skilled annotation experts who go through the data and make sure it is correct and is annotated well enough to be fed to a machine learning model.

An annotation platform is incomplete without providing insights and a better way to visualize the progress of the annotation, performance of the annotators, and data related visualizations. By providing a good overview of the annotation process through the annotation tool developed by Predictly, it always keeps one updated with what is going on throughout the different stages of the annotation process.

One of the biggest challenges in data annotation is the expense related to the process of annotating data. Manual Labelling, which is considered to be one of the most reliable and accurate processes of annotating data is highly expensive. Predictly offers the same solution in a highly cost-effective manner which comes with complete security of the data.

ML Operations

Our technology stack has the cutting edge libraries/frameworks, like Docker and Kubernetes, that empower us to enable Machine Learning at production level.

Highly standardized and efficient processes makes the machine learning faster.

Predictly realizes that unorganized piles of data can be reframed into organized & extremely useful information with the support of data annotation tools deployed through human assistance and machine learning capabilities.

 

What Else Do We Have?

ETL: A process in Data management that stands for ExtractTransform, and Load. It is a process in which the data is extracted from various data sources, transformed, and then finally, loaded into the Database system.

Extract: Extraction of Data is the first step of the ETL process. Data from several sources is extracted in several formats available like relational databases, No SQL, XML, and flat files. The data thus collected is stored in a very secure database and used when required.

Transform: Transformation is the second step of the ETL process. The raw data thus collected in the extraction phase has to be now organized and structured. A set of rules or functions are applied to the extracted data to convert it into a single standard format. Steps like filtering, cleaning, joining, splitting, and sorting are involved in the proves of transformation.

Load: Loading is the third and final step of the ETL process. In this step, the transformed data is finally loaded into a highly secured and managed database. Sometimes the data is updated by loading into the data warehouse very frequently and sometimes it is done after longer but regular intervals. The rate and period of loading solely depend on the requirements and vary from system to system

 

Data Processing: Data plays a highly critical role in taking key decisions in an organization. Transforming raw data into meaningful information is what Data processing is all about. Highly skilled professionals are required to apply different techniques for analyzing and processing raw and unstructured data.

Information Extraction: Information extraction is the process of extracting specific information from textual sources to feed the machine learning models. One of the most trivial examples is when your email extracts only the data from the message for you to add in your Calendar.

Gathering detailed structured data from texts, information extraction enables the automation of tasks such as smart content classification, integrated search, management, and delivery. It also helps and assists in data-driven activities such as mining for patterns and trends, uncovering hidden relationships, etc.

Data Combining: Ai operates by combining large amounts of data with fast, iterative, and intelligent algorithms. This combining process enables the software to automatically learn from the model or function of the data. The process simplifies data preparation for analysis, model development with modern machine learning algorithms, and text analysis integration into a final single output.

 

Data Replication: Also known as data augmentation, the existing data is taken and replicated in all the possible patterns and forms of data to create a learning experience for the machine learning models and make them able enough to understand the data in different scenarios and circumstances. This also multiplies the volume of the data for the machine learning model to be fed and built upon.

 

Data Cleaning:  Data is a highly useful resource but the fruitful use of that data to a great extent lays on the capacity of companies to give clean, precise, and usable data to workers to make continuous insights. Data of poor quality can prompt off base data examination results which can further lead to confused and inaccurate decision making for the machine learning model— both of which are negative to developers and data testers alike. Predictly assists in cleansing of the data like texts, images, etc.

Model Building: Without access to clean, accurate, and usable data, machine learning models don’t have a very good foundation to learn from. After all, AI is only as smart as the data it consumes. Predictly is equipped with highly diversified sets of models irrespective of frameworks to build models across multiple domains. Our process of the model building allows us to have the proper data science path of splitting data into train, validation, and test sets. Predictly has been working over highly diversified domains and thus has the flexibility to provide models starting from simple models like SVM and Decision tree to complex models like Transformers.

Model Experiments: We, at Predictly, are equipped with the ability to experiment with multiple models. Starting from smaller models to improving by creating and beating the baseline scores in each model development phase, our experiments include Hyper-parameter tuning which has been showing excellent and promising results in the enhancement of the model significantly.

Monitoring: Tools like Tensorboard to play around different models and monitor on what combination a particular model performs. This helps us to keep track of the data throughout the complete process.

With the development of the trained models to the deployment of the models in the respective industry, Predictly does it all!

Predictly usually does it through two basic ways:

·         Deployment Using REST API: This consists of a Prediction System and a Serving System where Prediction System is required to Process input data and make predictions while Serving System (The webserver) has the following benefits:

o   Serve predictions with scale in mind

o   Use REST API to serve prediction HTTP requests

o   Calls the prediction system to respond

Predictly makes it easy for you to deploy your machine learning model through REST API. The REST API has two systems. One is the prediction system and the other is the serving system. The prediction system takes your input data and runs through the machine learning system for the prediction whereas the serving system allows you to call the prediction system through HTTP requests and returns the required output.

 

·         Deploy using Containers: We use Docker type containers to host the trained machine learning model and use it for prediction service.

We also deploy machine learning models which use Docker like containers which will act as a prediction service. The docker container will run continuously and host the trained model, and whenever a prediction requires its response by running the prediction model and returning the output.

·         Model Serving:  

This process involves  Specialized web deployment for ML models

With tools like TensorFlow serving, Seldon we can serve machine learning models on-premise.

By using tools like Tensorflow serving we manage to deploy trained machine learning models easily. This deployment system is easy to establish and whenever there is a change required it’s very easy to integrate new models with just one command. It’s easily scalable and allows you to perform complicated tasks very quickly and efficiently.

One of the most important aspects of the AI model development is data. Therefore, it becomes very necessary for the organizations to gather and extract enough data from wherever they can to feed their machine learning models and thus make them better and efficient. More is the quality data fed to the model; more is the ability of the model to perform and higher becomes the efficiency of the model.

There might be an abundance of data around but it is usually raw and cannot be put into use to create state of the art AI models. Thus, the raw data has to be collected, transformed, analyzed, and experimented to be fed to the machine learning models for them to work with effectiveness and efficiency.

Image classification involves teaching an Artificial Intelligence (AI) how to detect objects in an image based on their unique properties. An example of image classification is an AI that detects how likely an object in an image is to be an apple, orange or pear.

 

Using the bounding box to easily move, rotate, duplicate, and scale objects by dragging the object or a handle (one of the hollow squares along the bounding box).

Semantic segmentation involves the process where all objects of the same type are marked using one class label while in instance segmentation similar objects get their own separate labels.

Landmark annotation works by placing points across an image to label objects within that image. This kind of labeling ranges from single points to annotate small objects, and also multiple points to outline particular details. Images for landmark annotation can include maps, faces, bodies, and objects.

Optical character recognition (OCR) is a technology that allows converting static documents, such as physical forms, into a format that’s searchable and editable.

Document classification refers to a process of assigning one or more labels for a document from a predefined set of labels. The main issues in document classification are connected to classification of free text giving document content.

Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish). Human and machine translation each have their share of challenges.

Named entity recognition (NER) helps you easily identify the key elements in a text, like names of people, places, brands, monetary values, and more. Extracting the main entities in a text helps sort unstructured data and detect important information, which is crucial if you have to deal with large datasets

Relationships are the grammatical and semantic connections between two entities in a piece of text. Predictly uses a combination of deep learning and semantic rules to recognize and extract the action that connects entities: their relationship.

Dependency parsing aims to predict these dependency relations between lexical units to retrieve information, mostly in the form of semantic interpretation. It analyzes the grammatical structure of a sentence, establishing relationships between “head” words and words which modify those heads.

Question answering is a computer science discipline within the fields of information retrieval and natural language processing, which is concerned with building systems that automatically answer questions posed by humans in a natural language.

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