Customize your AI augmented Intelligence Process Automation
Predictly understands how important it is to automate the processes in a business and Predictly is here to help businesses in implementing machine learning with no hassle, which reduces costs and optimizes the overall productivity.
How AI makes process automation easy
Predictly ML Ops Platform
- A 3W analysis to understand the problem statement and determining the ideal way of solving the problem with Machine learning and what will be the cost impact.
- Data Collection: Finding out the right data sources for the problem statement and what will be the best storage for the collected data.
- Data Annotation: Building a high quality dataset is as important as building the right model. We will use our Predictly Data Annotation Platform to effectively annotate data in a much less time and cost.
- Data Versioning: To keep track of the additional data and unlabeled data we will keep versioning data using DVC tools.
- Data Processing: Create features for machine learning models from the raw data. Apply different techniques of feature extraction and selection methods.
- Data Analysis: Run multiple experiments and data analysis techniques to understand the data more and prepare features. Figuring out the best metric, creating metadata are one of the tasks that will be curated in this phase.
- Build/Selection: Predictly starts building a simple model as the baseline and keeps on improving with different complex models as per the requirements.
- Train: Predictly takes care of the entire training process, which involves comparing models with the right metrics, tuning hyperparameters, understanding impact of overfitting and a lot more.
- Debugging: Before pushing a model into production, Predictly runs the final model against all sort of test cases to ensure it runs smoothly on the deployed platform
- Deploy: Predictly deploys the models in various methods such as REST API, Containers, Model serving etc.
- Model: With CI/CD/CT (Integration, Deployment and Training) we ensure the model keeps running smoothly in real time.
- Data: We constantly monitor the data distribution and whenever there is a change in data distribution we utilizes our CT (Continuous Training).
- Application: We continuously monitor the application, to track every functionality that works perfectly. And this is where we keep utilizing CI/CD to be on track.
Why should you use Predictly ML Ops?
Managed infrastructureA completely managed platform to deploy machine learning models on Cloud or on premise with minimum setup.
AccuracyAll deployed models went through rigorous testing to achieve best metric performance and make it work under real world conditions.
Optimized WorkflowAn optimized flow of machine learning operations to achieve the best performance in minimum time and less turbulence.
PrivacyPredictly ML OPS committed to protect its user data and it’s intellectual property with utmost priority.
Accelerated process automationWe accelerate the automation process with our Artificial Intelligence/Machine Learning enabled Hyper Automation platform.
How you can leverage our 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.
Use Predictly hyper automation for NLP and CV applications
Natural language processing
Text Classification
Machine Translation
POS Tagging
Named Entity Recognition
Relation Extraction
Dependency Parsing
Question Answering
Text Classification
Text classification is the process of classifying texts/documents into specific categories.
Machine Translation
Machine translation allows you to translate one language to another language or languages.
POS Tagging
POS tagging is the process of marking/tagging words in a sentence as corresponding to a particular part of speech.
Named Entity Recognition
NER is the process of identifying the named entities for the words/tokens in a sentence/text.
Relation Extraction
Relation extraction is the method of finding the relations between the named entities.
Dependency Parsing
Dependency parsing is the way to analyze the grammatical structure of a sentence based on the dependencies of the words/tokens.
Question Answering
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
Object Detection
Segmentation
Landmark Annotation
OCR Annotation
Image Classification
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
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
segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
Landmark Annotation
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.
OCR Annotation
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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