Machine Learning Model Integration with minimum effort.

Worrying about complexity of training, building, evaluating machine learning models?

Predictly offers an easy to integrate fully customizable machine learning model which can be integrated seamlessly either at your hosted cloud or through API.

Predictly has expertise in working with various types of machine learning models in both Natural language and Computer Vision, which helps you to rapidly develop a machine learning enabled application with less or no effort.

Our Offerings

Model Zoo

Predictly Model zoo has a wide variety of pre-trained models for hundreds of use cases which are readily available with multiple methods of usage option.

Solution APIs

All our models and complete use case solution pipelines come with an REST API option, by which you can easily consume our pre-trained model or any use case solution through a single API call.

Custom Machine Learning Models

You have your own requirements! Great, Predictly offers it’s modeling capabilities as it’s service to help you build machine learning models for your customized data and requirements and let it deploy to your provided hosting option.

Machine Learning Models

Natural Language Processing

Text classification is the process of classifying texts/documents into specific categories.

Machine translation allows you to translate one language to another language or languages.

POS tagging is the process of marking/tagging words in a sentence as corresponding to a particular part of speech.

NER is the process of identifying the named entities for the words/tokens in a sentence/text.

Relation extraction is the method of finding the relations between the named entities.

Dependency parsing is the way to analyze the grammatical structure of a sentence based on the dependencies of the words/tokens.

Question answering is a system which specifically tries to find out answers to the query. It can be both open domain and close domain.

Computer Vision

Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label. Typically, Image Classification refers to images in which only one object appears and is analyzed.

Object detection is a method in computer vision that deals with detecting instances of semantic objects of a certain class in digital images nd videos.

segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.

Landmark detection is a fundamental building block of computer vision applications such as face recognition, pose recognition, emotion recognition, head recognition to put the crown on it, and many more in augmented reality.

Optical character recognition or optical character reader is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo or from subtitle text superimposed on an image.

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