Predictly offers a range of services and products to different kinds of industries. Only to improve AI technology to all over domains/industries. List of products & services provided by Predictly.

  • Pre-trained models based on project requirements
  • Datasets based on customer requirements
  • Data annotation services.
  • Act as an AI-transformation service if any company wants to adopt or improve their business by using AI.

What requirements organizations need when they want to use or adopt AI-based models to their business process to get experience to increase productivity, understanding of customers, etc. all are available in one place. Yeah, Predctly provides all services based on organizations’ needs.


  • If organizations want datasets to build their own models, then they can find that service at predictly.
  • If organizations have a dataset but they do not know how to build a build? then Predictly builds a model with that dataset.
  • If organizations want to improve the quality of data they have, Predictly collects data from organizations and improve data quality by using different techniques.
  • If organizations have smaller datasets but they want to build models, so predictly offer service for this issue also. Predictly take company data and then increase.
  • If organizations need annotated data for their models but they have only unstructured data, so Predictly offers annotation services to different domains.
  • Organizations have knowledge on building AI-models, they want pre-trained models, Predictly also provides Pre-trained models to industries who want.
  • If organizations don’t have any knowledge of machine learning or AI technologies but they want to utilize the power of AI to business process, in this case, Predictly act as an AI-Transformation adviser to organizations.

Data is any value, text, image, video, audio, etc. Data is a major part of data analysis, to build machine learning, deep learning, artificial intelligence-related models. Because these models will gain knowledge from data by extracting important and relevant information.

Dataset is a collection of data, such as a bunch of images, chunks of sentences/documents, etc. for building or creating AI-related models. We need different datasets based on project requirements.

A number of firms label data and provide annotation services. Through a number of channels is the annotation being done throughout the industry. At the same time, it is time consuming and hectic and thus a number of companies like to go ahead with the prelabeled datasets available in the market. 

The most difficult task is to find a dataset that is qualitative enough to train a model which can be effective and nothing less than great! Pre-trained and labeled datasets offered by Predictly is one of the products that is highly qualitative and appropriate for the businesses looking for prelabeled datasets which are actually useful. The datasets are trained under extremely stringent measures of quality and security with high precision and accuracy. The complete process undergoes a quality check procedure to keep up with the standards and utility of the data. The datasets are available in various formats thus making it flexible  enough to be used.

For this question, there is no basic answer. It will rely profoundly upon what sort of data we have, and what techniques we are utilizing. The negligible size of the dataset can rely upon numerous variables, for example, the complexity of the model we are attempting to construct or build, the performance of the model we are focusing on, or once in a while it can even be because of the time span that is available to us. Be that as it may, each AI or deep learning algorithm consistently requires huge amounts of quality data.

For some Machine Learning or AI models to work, we need tens or a vast number of remarkable data points for it to learn knowledge from. Contingent upon project prerequisites or AI algorithms, the vendor could likewise pull public information/data from the Internet to improve the model performance; however, this is a significant inquiry.

Predictly will gather the required amounts of data to build models based on project requirements of clients or customers as well as data formats. we are using different strategies to collect or gather data.

Extracting data from different websites by using web scraping techniques.

We can buy dataset which is already available for us, that dataset should be suitable for project requirements.

Another strategy is to, collect data from different databases of a particular organization/company and create a dataset for the project.

Predictly uses any one of these strategies at the stage of data collection for every project. If data is not enough, then predictly improve that dataset by using different kinds of data augmentation techniques based on data format like image, text, etc.

Artificial Intelligence and related field algorithms are no enchantment or magic. They need data to work, and they must be comparable to the data that we feed in. There are various techniques and sources to gather the correct information, depending upon our project targets. At any rate, the more data we have, the better the chances that our ML/AI model will perform well. Suppose we have questions about the quantity and quality of our data. In that case, we can take help from data scientists to assess our gathering or gathered datasets and find the ideal approach to get to outsider data, if essential.

Other than having the perfect quantity and type of data, we ought to likewise ensure we are gathering data in the correct configuration. Imagine we have taken a great many ideal pictures of cell phones (great goal and white background) to prepare a computer vision model to recognize them in images. At that point, we find that it won’t work because the actual use case was recognizing individuals holding cell phones in different lighting/contrasts/backgrounds, and not merely the cell phones. Our collected data effort would be almost useless, and we should begin once again. Additionally, we ought to comprehend if bias exists in the information being gathered because AI algorithms will learn that bias.

Predictly will increase client dataset size if the client does not have enough data for their project. We can increase the dataset by using different kinds of data augmentation techniques depending on the data format. Data augmentation is the technique of increasing the size of data used for training a model. Because every data format has different data augmentation methods. That means augmentation techniques for images are different from text augmentation techniques.

Few image data augmentation techniques are

  • scaling,
  • cropping,
  • flipping,
  • rotation,
  • saturation,
  • brightness, etc.

Few text augmentation techniques are

  • Replacing words or phrases with their synonyms,
  • Back-translation method to generate more training data to improve translation model performance.
  • Text generation, etc.

Artificial intelligence models expect data to make precise predictions. The amount of data and the kind of data required totally rely upon the project. Since this will appear to be unique for each problem statement, we’ll have to think about various types of inquiries, for example,

  • What kind of information do we require for the model building?
  • What amount of data do we require for the model?

A fair vendor will let us know whether we need better quality information to help an AI solution make reliable predictions. For each situation, data is the fuel. Furthermore, a modern algorithm without data resembles a Lamborghini without gas—we’re going no place quickly.

Data annotation is the process of labeling all the data points of a dataset with one or more labels or classes. Here datapoint may be a single sentence, a single document, one image, etc.

Data annotation is a significant part of data transforming from unstructured to structured data. This is really very important in AI/ ML projects to enable effective models.

Data annotation or data labeling gives the first step to providing an AI model with what it needs to understand about the data. By frequently feeding annotated data to AI train models, we’re ready to build up a model that can start getting more intelligent after some time.

There is a wide range of sorts of annotations, depending upon what kind of structure the data is in. Data may be in the form of text, image, video,  audio, etc. we can annotate all forms of data with corresponding labels or tags. It can go from picture and video annotation, text classification, semantic annotation, and content categorization.

  • Text annotation for Natural language processing:-
    • Text annotation is simply done for Natural Language Processing (NLP) tasks by machines to understand the communications of humans of their native languages. in this annotation process annotators who are doing the labeling, processes are labeling the text format dataset by using respective tags of projects.
    • For example, annotate sentences with positive, negative, neural labels when we create a dataset for sentiment analysis.
  • Image annotation for object detection and recognition:-
    • The main objective of image annotation is, labeling the images with corresponding labels. so AI/ML models will recognize objects within the image. For example, our problem statement is to recognize cats or dogs in the input image. so we have only cats & dogs image datasets. In this case, we can annotate images with cat, dog labels by using image annotation techniques.
  • Video annotation for high-quality visualization training:-
    • video annotation is the same as image annotation and text annotation. the main objective of video annotation is making moving objects within the video recognizable to machines. this type of data used by computer vision algorithms.annotators will annotate different kinds of moving objects in videos to recognize their movements.
  • Semantic annotation:-

semantic annotation is the process of annotating different concepts within the text dataset like people, objects, locations, company names, etc.Machine learning (ML) or Artificial intelligence models use this annotated data as a reference to their algorithms, to recognize annotated concepts on new sentences or documents. This is mostly used to find relationships between words, train chatbots, etc.

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