At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous.

In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.

What is Image Annotation?

So, right here we will discuss about the applications of the image annotation, but before we proceed, we need to review the definition of image annotation and its use in AI industry. Image annotation is the process of making the object of interest detectable and recognizable to machines.

And to make such objects recognizable in the images, they are annotated with added metadata for the description of the object. And when a huge amount of similar data is feed into the model, it becomes trained enough to recognize the objects when new data is presented in real-life situations.

5 APPLICATIONS OF IMAGE ANNOTATION

Annotated images are mainly used to make the machine learn how to detect the different types of objects. But as per the AI model functions, ML algorithms compatibility and use in the various industries, image annotation applications also differ that all about we will discuss here below with the annotation types.

Detect the Object of Interest

The most important application of image annotation is detecting the objects in the images. In an image, there are multiple things, or you can say objects, but every object would be not required to get noticed by the machines. But the object of interest need to be get detected, and the image annotation technique is applied to annotate and make such objects detectable through computer vision technology.

Also Read: What Is Computer Vision: How It Works in Machine Learning and AI

Recognize the Types of Objects

After detecting the object, it is also important to recognize what types of objects it is, humans, animals or non-living objects like vehicles, street poles and other man made objects visible in the natural environment. Here again image annotation helps to recognize the objects in the images.

Though, object detection and recognition runs simultaneously, and while annotating the objects in various cases, the notes or metadata is added to describe the attributes and nature of the object, so that machine can easily recognize such things and store the information for the future references.

Classifying the Different Objects

It is not necessary all objects in an image belong to the same category, if a dog is visible with man, it needs to be classified or categorized to differentiate both of them. Classification of the objects in the images is another important application of image annotation used in machine learning training.

Also Read: What is the Importance of Image Annotation in AI And Machine Learning

Along with image classification, the localization of objects is also done through image annotation practice. In image annotation, there are multiple techniques, used to annotate the objects and classified into the different categories helping the visual perception based AI model detect and categorize the objects.

Object Segmentation of Single Class

Just like object classification, objects in the single class need to be segmented to make it more clear about the object, its category, position and its attributes. Semantic segmentation image annotation is used to annotate the objects with each pixel in the image belongs to a single class.

Also Read: What is Semantic Image Segmentation and Types for Deep Learning

The main applications of image annotation is to make the AI model or machine learning algorithm learn with more accuracy about objects in the images. For semantic segmentation, image annotation is basically applied for deep learning-based AI models to give precise results in various scenarios.

Also Read: How To Label Data For Semantic Segmentation Deep Learning Models

Recognizing the Humans Faces

AI cameras in smartphones or security surveillance, are now able to recognize the face of humans. And do you how it became possible in AI world? Thanks to image annotation, that makes the humans faces recognizable through computer vision with the ability to identify the person from the database and discriminate them among the huge crowd from the security surveillance system perspective.

In image annotation for face recognition algorithms, the faces of humans are annotated from one point to another point measuring the dimension of the face and its various points like chin, ears eyes, nose and mouth. And these facial landmarks are annotated and provided to the image classification system. Hence, image annotation is playing another important role in recognizing the people from their faces.

Also Read: How To Select Suitable Machine Learning Algorithm For A Problem Statement

TYPES OF IMAGE ANNOTATION

I hope you got to know the applications of image annotation in the world of AI and machine learning. Now you should know what are the types of image annotations used to create the machine learning training datasets for deep learning based AI models? And we will also discuss here the application of different types of image annotation into various industries, fields and sectors with uses cases of AI-based models.

Also Read: What are the various Types of Data Sets used in Machine Learning

Bounding Box Annotation to Easily Detect the Objects

Bounding box annotation is one of the most popular techniques used to detect the objects in the images. The object of interest are annotated either in a rectangular or square shape to make the object recognizable to machines through computer vision. All types of AI models like self-driving cars, robots, autonomous flying objects and AI security cameras relying on data created by bounding box annotation.

Semantic Segmentation to Localize Objects in Single Class

To recognize, classify and segment the objects in the single class, semantic image segmentation is used to annotate the objects for more accurate detection by machines. It is actually, the process of diving the images into multiple segments of an object having the different semantic definitions. Autonomous vehicles and drones, need such training data to improve the performance of the AI model.

Also Read: How To Improve Machine Learning Model Performance: Five Ways

3D Point Cloud Annotation to Detect the Minor Objects

The image annotation applications not only include object detection or recognition, even can also measure or estimate the types and dimensions of the object. 3D point cloud annotation is the technique helps to make such objects detectable to machines through computer vision. Self-driving cars are the use case, where training data sets is created through 3D point cloud annotation. This image annotation helps to detect the object with additional attributes including lane and sideways path detection.

Landmark Annotation to Detect Human Faces & Gestures

Landmark annotation is another type of image annotation techniques used to detect human faces. The AI models like AI cameras in security surveillance, smartphones and other devices can detect the human faces and recognize the gestures and various human possess. Landmarking is also used in sports analytics to analyze the human possess performed while playing outdoor games. Cogito provides the landmark point annotation with the next-level of accuracy for precise detection of human faces or their poses.

3D Cuboid Annotation to Detect the Object with Dimension

Detecting the dimensions of the object is also important for AI models to get a more accurate measurement of various objects. The 2D images are annotated with capturing all the dimensions visible in the image to build a ground truth dataset for 3D perception on the objects of interest. Again autonomous vehicles, AI robots and visual perception models used to detect the indoor objects like carton boxes with the dimension need such annotated images, created through 3D cuboid annotation.

Polygon Annotation to Detect Asymmetrical Shaped Objects

Similarly, polygon annotation is used to annotate the objects that are in irregular shapes. Coarse or asymmetrical objects can be made recognizable through the polygon image annotation technique. Mainly road marking or other objects are annotated for the self-driving cars. And autonomous flying objects like drones, viewing the objects from ariel view can detect or recognize such things when trained with training data sets created through polygon annotation for precise object detection.

Polyline/Splines/Line Annotation for Lane or Path Detection

Lines, Polylines and Splines are all similar types of image annotations used to create the training data sets allowing computer vision systems to consider about the divisions between important regions of an image. The boundaries, annotating lines or splines is useful to detect lanes for self-driving cars. Road surface marking that are indicating the instructions of driving on the road need to also make understandable to autonomous cars. Polyline annotation that divides one region from another region.

The right applications of image annotation is possible when you use the right tools and techniques to create high-quality training data sets for machine learning. And Cogito is the industry leader in human-powered image annotation services with the best level of accuracy for different AI models or use cases. Working with a team of well-trained and experienced annotators, Cogito can produce the machine learning training data sets for healthcare, agriculture, retail, automotive, drones and robotics.

In today’s era, mechanization taking place everywhere with a new age of development in more automated systems, applications, and robots, etc. Machine learning and AI are the leading cutting-edge technologies giving automation a new dimension with more tasks performed by machines itself.

Though, nowadays many tasks can be independently performed by AI-enabled devices, systems or machines without the help of humans. But developing such machines is not possible without the help of humans. So, Human-in-the-Loop or HITL is a model or concept requires human interaction.

What is Human-in-the-Loop?

Human-in-the-loop (HITL), basically you can say, is the process of leveraging the power of the machine and human intelligence to create machine learning-based AI models. HITL describes the process when the machine or computer system is unable to solve a problem, needs human intervention like involving in both the training and testing stages of building an algorithm, for creating a continuous feedback loop allowing the algorithm to give every time better results.

Humans annotate or label data then give to the machine learning algorithm to learn from such and take decisions from such predictions. And then humans also involve in tuning the model to improve its accuracy. And finally, these people test and validate the model by scoring its outputs, when machine learning algorithms not able to make the right decisions or gives incorrect decisions.

Also Read: How To Select Suitable Machine Learning Algorithm For A Problem Statement

Why Human-in-the-Loop Machine Learning is used?

If you have a sufficient amount of datasets, an ML algorithm can easily make decisions with accuracy just learned from these datasets. But before that machine needs to get learn from the certain amount and quality of data sets, how to properly identify the right criteria and thus comes to the right results.

This where Human-in-the-Loop machine learning is used to the combination of human and machine intelligence creating a continuous circle where ML algorithms are trained, tested, tuned, and validated. In this loop, with the help of humans, the machine becomes smarter as well as more trained and confident to take the quick and accurate decisions when used in real-life and also help to train the algorithms.

Also Read: How to Improve Accuracy Of Machine Learning Model

How Human-in-the-Loop Machine Learning is Used Today?

Human-in-the-loop is basically integrated two machine learning algorithm processes – supervised and unsupervised learning. In supervised machine learning, labeled or annotated data sets are used by ML experts to train the algorithms, so that it can make the right predictions when used in real-life.

Also Read: How Model Predictions are used to Increase Data Labeling Speed and Improve Accuracy

While on the other hand, in unsupervised machine learning there is no labels are given to the learning algorithm, leaving it on its own to find structure in its input and memorize the data in its own ways.

In HITL, initially, humans label the training data for the algorithm which is later fed into the algorithms to make the various scenarios understandable to machines. Later humans also check and evaluate the results or predictions for ML model validation and if results are inaccurate humans tune the algorithms or data is re-checked and again fed into the algorithm to make the right predictions.

Also Read: How to Validate Machine Learning Models:ML Model Validation Methods

Why Human-in-the-Loop is Important for Machine Learning?

Doing a machine learning process without human inputs is not possible. Algorithms cannot learn everything unless provided as per its compatibility. For example, a machine learning model cannot understand raw data unless humans explain and make it understandable to machines.

Also Read: How To Improve Machine Learning Model Performance: Five Ways

Here, the data labeling process is the first step in creating a reliable model trained through algorithms, especially when data is available in an unstructured format. Actually, an algorithm cannot understand the unstructured data like texts, audio, video, images and other contents that are not properly labeled.

Hence, the human-in-the-loop approach is required to make such data comprehensible to machines. These data are labeled as per the desired instructions like what is seen in the images, what is spoken in the audio or video using the data labeling or image annotation techniques to label such data.

Also Read: Why Data Annotation is Important for Machine Learning and AI

When Human-in-the-loop Machine Learning is used?

Human-in-the-loop is not the concept you can implement in every machine learning project. Mainly HITL approach is used, when there is not much data available yet, human-in-the-loop is suitable because, at this stage, people can initially make much better judgments than machines are capable of.

Also Read: How Much Training Data is Required for Machine Learning Algorithms

And using this, humans produce machine learning training data sets helping the machine to learn from such data. And human in the loop deep learning is used when humans and machine learning processes interact to solve one or more of the following scenarios:

• Algorithms are not understanding the input.
• When data input is interpreted incorrectly.
• Algorithms don’t know how to perform the task.
• To make humans more efficient and accurate.
• To make the machine learning model more accurate.
• When the cost of errors is too high in ML development.
• When the data you’re looking for is rare or not available.

Human-in-the-Loop for Different Types of Data Labeling

As per the algorithms, different types of datasets in machine learning training are required. And the human-in-the-loop approach is used for such different types of the data labeling process. If you want to train your model to identify or recognize the shape of objects like an animal on the road or other objects, then bounding box annotation is best suitable to make them recognizable to machines.

Also Read: How to Measure Quality While Training the Machine Learning Models

While, on the other hand, if you have to classify the objects in a single class, you have to use the semantic segmentation annotation suitable for computer vision to train the visual perception based ML model. Similarly, to create facial recognition training data sets, landmark annotation is used. In language or voice-recognition machine learning training, text annotation, NLP annotation, audio annotation, and sentiment analysis is used to understand what humans are trying to say in different scenarios.

And when such data is labeled, annotated or make usable to machines, chatbot or virtual assistant like AI devices are developed to communicate with humans. Humans-in-the-loop can create different types of training data sets for different types of machine learning models built for different fields.

Why Human-in-the-Loop Services by Cogito?

AI is getting integrated almost every field around the world, but we still required Human-in-the-Loop, especially to produce and feed the training data into the algorithms at the initial stage of model development. Here, Cogito provides wide-ranging services for human-in-the-loop machine learning and human in the loop AI comprising text, videos, data and image annotation services for AI development.

Cogito can produce high-volume of training datasets with a fast turnaround capability and scalable solution with best-in-class accuracy. Cogito follows the most feasible data labeling process while following all the international data security standards to ensure the quality and privacy of data at various stages of processing. Here all the outputs are carefully reviewed by our experts before it is sent to our clients.

As per the international organization reports, globally more than fifty percent population is living in urban areas, and by 2050, more than two-third population will live in cities providing big investment opportunities of tech development companies.

As per the International Data Corporation report, by 2021, the spending on smart cities likely to reach more than $130 billion to make the urban areas and cities more livable with advance infrastructural facilities and society management system.

AI in smart cities is going to play big role in making urbanization smarter aim to be sustainable growth making the cities equipped with advance features to live, walk, shop and enjoy a safe and more convenient life in such environment.

Actually, while developing cities to make smart, several challenges like administration, sanitation, traffic congestion, security surveillance, parking management and many more that AI can help to provide a sustainable solution to habitants.

APPLICATIONS OF AI IN SMART CITIES

The use of artificial intelligence in smart cities can be life changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of system.

Also Read: What Is Computer Vision: How It Works in Machine Learning and AI?

Advance Security Camera & Surveillance System

AI-enabled cameras and sensors can keep an eye on surroundings to enhance the security level in the city’s neighborhoods. Such cameras can recognize the people and their faces or track the unusual activities done by them at restricted areas.

High-resolution AI security camera system can track movement of all registered vehicles and can monitor crowd density and cleanliness of public spaces round-the-clock. And thanks to the historic data available from different departments of the city, police can predict category and intensity of crimes monitoring all such activities in a particular area.

Vehicle Parking and Traffic Management System

In cities most of the people have their own cars and also there is huge movement of commercial vehicles to transport people as well as goods. So, parking of such vehicles and traffic management is another space where AI can help.

Using the road surface sensors or CCTV cameras incorporated into parking spots allow cities to create a real-time parking and traffic maps, helping drivers to save their time by avoid waiting to find an empty space to move smoothly or be in traffic.

AI-supported traffic sensor systems can use cameras to collect real time data of vehicles on road, and send it to a control centre, which collates the data fed from other points and adjusts the signal timings to ensure smooth flow of vehicles.

Smart transportation also includes public sector, and thanks to AI, there are lots of opportunities for improvement of public transit. And now cab services like Uber are also using AI to give a better riding experience to its customers.

Autonomous Flying Objects for Ariel View Monitoring

AI-enabled drones or autonomous flying similar objects can be used to monitor the inner-city and houses or other concerning areas. The in-built cameras in drones help to provide the real-time visuals of the different locations where humans cannot reach easily or quickly helping administration and security departments to take timely actions.

Autonomous flying drones can track humans, monitor traffic movement and provide the 2D aerial view imagery mapping for better urbanization of cities. It can be used for advance security and surveillance for security departments and crime squad.

Also Read: How to Improve Computer Vision in AI Drones Using Image Annotation Services?

Face Detection Cameras and Movement for Public Safety

AI in face recognition technology is capable to detect the different people from their faces disclosing their personal identities. AI in security cameras or drones can recognize the human faces and match with the database to trace his identity and authenticate the person entering in the cities, societies and other restricted areas.

Landmarking annotation is the technique used to train the AI-based model in face recognition system. And to make cities smart, such high-level of face detection technology is necessary to make the surroundings more safe and private.

Smart Waste Management and Disposal System

People living in cities produce huge amount of waste that is another challenge for the urban management to find the right way of managing garbage and keep environment clean to maintain the hygiene level in the society. AI-enabled cameras can detect trash thrown on the street and recognize the types of garbage for categorization.

But AI-enabled installing sensors on waste bins can make waste collection more efficient. Authorities can receive notifications when the bins are about to be filled and ensure reducing of operational costs by eliminating unnecessary pickups, providing dynamic collection routes and schedules for optimization of waste management.

Better Governance and Planning Management

While developing the cities or urban townships, AI or machine learning techniques can be used to map land use across time to generate crucial insights using the satellite imagery and aerial view 2D or 3D images of geographical areas.

Also Read: What are the various Types of Data Sets used in Machine Learning?

The algorithms trained through machine learning can analyze satellite images for city planning and development with scope to adjust the formation based on changing data, areas prone to calamities like flooding, earthquake and storms. Such real-time as well as historical data can continuously be monitored to enable the better governance.

Summing-up

AI in smart cities development can play a crucial role in urban planning, development and management with advance security system, traffic monitoring and disposal management to make the societies more secured and livable with access and control of their home and other activities making their living experience more enjoy and comfortable.

And to develop the AI-enabled systems for smart city development, machine learning and AI companies need huge amount of training data for smart cities to train the models like drones, AI security camera and face recognition system to work with accurately and provide the correct information making the smart cities really smart for sustainable growth.

And Cogito can provide such training data sets to train the AI and machine learning system can be used for smart city development. It is actually involved in data labeling job with expertise in image annotation services to annotate the object of interest in images to make it recognizable to machines through computer vision technology.

Also Read: How Computer Vision Can Improve Accuracy of Diagnosis in Medical Imaging Analysis?

Cogito a works with advance data labeling system complying all the data security and protection standards like to make sure provide the high-quality training data sets produced in highly secured and protected environment helping machine learning developers to build a successful and most reliable AI models at low-cost.

Also Read: Why Outsourcing Your Data Annotation Needs to GDPR, CCPA or SOC Type Compliant Companies?

Thanks to AI and machine learning, computer vision technology is getting upgraded with improved version of visualizing making perception through machines reliable. Actually, this is completely related computer based visual processing of objects.

What is Computer Vision in AI and Machine Learning?

Computer vision is the simply the process of perceiving the images and videos available in the digital formats. In Machine Learning (ML) and AI – Computer vision is used to train the model recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use.

The main purpose of using the computer vision technology in ML and AI is to create the a model than can work itself without human intervention. The whole process involves methods of acquiring the data, processing, analyzing and understanding the digital images to utilize the same in the real-world scenario.

How Computer Vision System Work?

You can say computer vision is used for deep learning to analyze the different types of data sets through annotated images showing object of interest in an image. It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

This process depends subject to use of various software techniques and algorithms, that are allowing the computers to recognize the patterns in all the elements that relate to those labels and make the model predictions accurately in future. Computer vision can be only utilize only with image processing through machine learning.

What is Difference Between Computer Vision and Image Processing?

Both are part of AI technology used while processing the data and creating a model. The difference between computer vision and image processing is Computer vision helps to gain high-level understanding from images or videos.

For instance, object recognition, which is the process of identifying the type of objects in an image, is a computer vision problem. In computer vision, you receive an image as input and you can produce an image as output or some other type of information.

Whereas, image processing doesn’t need such high level of understanding of image. In fact it is the sub-field of signal processing but also applied to images. For an example, if you have a noisy or blurred images, then under image processing the deblurring or denoising is done to make the object in the image clearly visible to machines.

Image process task involves filtering, noise removal, edge detection, and color processing. In entire processing the you receive an image as input and produce another image as an output that can be used to train the machine through computer vision.

The main difference between computer vision and image processing are the goals (not the methods used). For example, if the goal is to enhance the image quality for later use, which is called image processing. If the goal is to visualize like humans, like object recognition, defect detection or automatic driving, then it is called computer vision.

Application and Role of Computer Vision in Artificial Intelligence

The applied science of computer vision is expanding into multiple fields. From AI development to machine learning, it is playing a significant role in helping the machines identify the different types of objects in their natural environment.

From simple home task to recognizing human faces, detecting the objects in autonomous vehicle, or combating with enemies in war, computer vision the only technology giving an edge to AI-enabled devices to work efficiently.

The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flaying like drones and manufacturing etc.

Actually, to create the computer vision-based model the labeled data is required for supervised machine learning. And image annotation is the data labeling technique used for creating such labeled images for computer vision.

Cogito is one the companies providing the data annotation service for computer vision providing the image annotation solution for AI and machine learning. Rendering the high-quality training data using the best tools and techniques allowing computer vision to help algorithms train the model perform accurately in real-life use.

AI in healthcare is now playing a life-sustaining role helping people to get the accurate treatment with timely diagnosis of various types of diseases. Similarly, machine learning in healthcare is becoming more imperative covering more types of disorders in the body helping people take precautions and well-timed treatments.

Machine Learning (ML) in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping doctors to recommend the right treatment. But there is more improvements required in this sub-field of healthcare sector.

Actually, machine learning algorithms is lying under the hood of high-quality medical training data sets, and with further advances in parallel computing and augmentation of training data sets ML will improve the dental image analysis. So, here we will discuss how dental imaging can be improved with machine learning.

Availability of High-quality Training Data Sets

The most important inputs to improve the accuracy of AI-based models prediction using dental images is – a high-quality training data sets, only that can help ML algorithm recognize the patterns and store in its virtual memory to use in the real-life.

As much as quantitative and qualitative data will be used to train the model, the accuracy will improve in machine learning for dental image analysis. And the healthcare training data for such needs is created with proper labeling of object of interest using the annotation technique to highlight the affected area.

In dentistry, the affected or damaged teeth conditions are outlined in the x-ray images by experienced radiologist, once such data sets get ready, it is used in ML. The ML algorithm learn from varied types of annotated dental x-rays, and learn from such source data, that is further used to detect when shown such x-rays.         

Using the Right Machine Learning Algorithm

The machine learning algorithms can only understand and learn the training data sets and its use. If you choose the inapplicable algorithm your AI project will be failed or will give the inaccurate results, which has no room in healthcare sector.

So, train your model with efficient dental image analysis algorithms as per the availability of types of data and model validation system. And to improve the machine learning algorithm performance, a huge amount of data is required.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

Implementing the CAD and CNN Systems

CAD or Computer-aided design systems are one the with potential AI application in healthcare, helps to analyze the medical images providing the radiologist another reliable opinion as a support to enhance the accuracy of analysis.

Though, commercially available dental CAD systems are available in the market and at the same time research on efficient dental image analysis algorithms continues. And to analysis the dental illness various techniques of image annotation is used.

Semantic segmentation is one of them used for detection, classification and segmentation of objects (teeth) in dentistry. But again choosing the right algorithm is again the important aspect to improve the dental imaging analysis.

Convolutional neural networks (CNN) is one of the most successful machine learning algorithms for object recognition in machine learning. It is a variation of a multilayer artificial neural network with Convolutional layer. This algorithm is particularity efficient in extracting features, regardless of their location in the image.

And this algorithm has shown great performance in image recognition and classification. They also have solved the thought-to-be-impossible problem of facial recognition. The amount of healthcare training data is the main concerning factor for CNN.

But with the increase in the number of increasing training data images up to millions, such algorithm starts recognizing the objects with unbelievable precision. Google image search algorithm is the best example of such “data-driven” training.

Yes, off-course improvements is architecture also improve performance and accelerate the training process, but training data again remains the key factor.

State-Of-The-Art Facilities and Practice Use

To improve the performance of  machine learning in dentistry the well-sourced architectures for dental image analysis systems is required at enterprise level. As the performance of AI model can vary in terms of sensitivity and specificity depending on the task, imaging modality and algorithms used to develop such models.

Hence, state-of-the-art facility is important to build the suitable model for automated medical imaging analysis machines in dental care and treatment. Similarly, the practice of using the training data into right algorithm is also crucial improving the overall performance and accuracy of dental image analysis algorithms.

And acquiring the right training data from the right source at the right price helps to build a fully-functional AI-enabled ML model for dental imaging analysis. Cogito is the company providing data annotation services with track record of supplying the high-quality training data sets for healthcare including dental image analysis.

For machine learning dentistry, Cogito has highly-skilled and experienced radiologists to annotate to dental x-rays manually using the best software for developing the huge amount of data sets as a dental image with best level of accuracy.

Training data is the key input to machine learning (ML), and having the right quality and quantity of  data sets is important to get the accurate results. The larger the training data available for ML algorithm, it will help model to perceive the diverse types of objects making easier to recognize  when used in real-life predictions.

But the question here is, how will you decide how much of training is enough for your machine learning. As insufficient data will affect your model prediction accuracy while more than enough data can give best results but can you manage the big data or huge quantity of datasets and it also required deep learning or more complex way to fed such data into algorithms.

Actually, there are many factors decide how much training data is required for machine learning like your model complexity, machine learning algorithms and data training or validation process. And in some cases how much data is required to demonstrate that one model is better than another. All these factors considered while choosing the right amount of datasets let we discus more elaborately to find how much data is enough of ML.

Depends on Complexities of Problem and Learning Algorithms

One of the most important factor while selecting training data for machine learning is complexity of problem means the unknown underlying function that relates to your variable inputs to the output variable as per the ML model type.

Also Read : What are the various Types of Data Sets used in Machine Learning?

Similarly, complexity of machine learning model algorithm is another important factor considered while choosing the right quantity of data sets. Actually, the algorithm used to inductively learn the unknown underlying mapping function from specific examples to make the best use of training data and integrate the same into the machine learning model.

Using the Statistical Heuristic Rule

In statistical terms there are many components considered like factor of the number of classes, factor of number of  input features and factor of the number of model parameters. And there are statistical heuristic methods available that allow you to calculate a suitable sample size.

In factor of the number of classes, there must be X independent examples for each class, where x could be tens, hundreds or thousands depending up on your parameter range. While input features there must be X% more examples than there input features and in model parameters there must be independent examples for each parameter in the model.

Model Skill vs Data Size Evaluation

While choosing the training data set for machine learning you can design s study that can evaluate model skill required against the size of training dataset. To perform this study plot the result of your model prediction, as a line plot with training dataset size on the x-axis and model skill on the y-axis that will give you an idea how much the quantity of data affects the skill of the model while solving a specific problem with machine learning.

You can use a learning curve in which you will be able to project the amount of data required to develop a skillful model or perhaps how small data you actually needed before touching an inflection point of diminishing returns. So, you can perform the study with available data and single performing algorithms like random forest and suggest you to develop a robust models in the context of well-rounded understanding of the problems.

More Data Required for Nonlinear Algorithms

Nonlinear algorithms are usually known as one the most powerful machine learning algorithms. As they are capable to learn the complex nonlinear relationships between inputs and output features. If you are using the nonlinear algorithms you need adequate amount of data sets and need to hire machine learning engineer that can work with such applied mathematics.

Also Read : How to Create Training Data for Machine Learning?

Such algorithms are often more flexible and even nonparametric means they can find out itself how many parameters are required to model your problem in addition to the values of those parameters. The predictions with such models vary based on the particular data used to train them resulting lots of data required for such model training.

Don’t Wait for More Data, Get Started what you have

It is not necessary you will get sufficient amount of training data for your ML and waiting to acquire such data for long days is not a sensible decision. Don’t let the problem of the training set size stop you from getting started on your model prediction problem solving.

Get started with the data you can, use what you have, and check how effective models are on you problem. Acquire something then take action to understand better what you have with for further analysis and then increase the data you have with augmentation or collect more data from your domain to make your model training more accurate.

Conclusion

The quality and quantity of training data is one of the most important factor machine learning engineers or data scientist are taking into the serious consideration while developing a model. However, in coming years it would become more clear how much training data is sufficient for machine learning model development but it is clear now “the more the better”. Hence, if you can acquire as much data and utilize the information it would be better for you, but waiting for bid data acquisition for longer time can delay your projects.

Also Read : How To Hire A Good Data Scientist: Five Easy Steps

Cogito is one the companies providing the high-quality training data sets for machine learning and AI. It is involved in data collection, classification and categorization with image annotation services to provide the well-supervised training data at affordable cost. It is providing the training data for all leading sectors including healthcare, automobile, agriculture and retail sector ready to adopt the machine learning or AI-based automated systems.