Intelligent process automation with machine learning
A fully fledged machine learning platform to enable businesses automate their process and make decisions driven by data.
A completely managed platform to deploy machine learning models on Cloud or on premise with minimum setup.
All deployed models went through rigorous testing to achieve best metric performance and make it work under real world conditions.
An optimized flow of machine learning operations to achieve the best performance in minimum time and less turbulence.
Predictly ML OPS committed to protect its user data and it’s intellectual property with utmost priority.
We accelerate the automation process with our Artificial Intelligence/Machine Learning enabled Hyper Automation platform.
Off-the shelf
trained models
Explore our off-the shelf already trained model with pre-annotated datasets. Which offers a wide range of applications like Legal clause classification, Resume entity recognition and many more.
Customized inference
APIs
You have your own business requirements and use case to solve, We are happy to provide you the customized Machine learning model in an REST API form.
Fully customizable
end-to-end ML solution
Predictly offers you to solve your entire business problem/use case with Machine learning and deploy it as an application.
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.
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 and 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.