Machine Learning
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** artificial intelligence and machine learning solutions **
Most of the AI/ML problems are either related to regression, classification and clustering on the machine learning side or a Neural Network approach using CNN, RNN, LSTM on the Deep Learning side. Here at Zummit we work on almost all areas of AI/ML including the latest areas like Generative Adversarial Networks (GAN), Natural Langauge Processing (NLP), Transformers, BERT etc

The problems could be related to Financial domain, Healthcare, Manufacturing, Travel & Hospitality or even retail like e-commerce and hyper local solutions. Some of the projects we work with are related to Face detection, emotion detection, autonomous driving (self-driving cars), disease prediction, financial models like stock markets etc

The engineers at Zummit Infolabs are focused towards researching on employing the cutting edge technologies to solve the problems and requirements of AI/ML requirements from various domains. The specialists at Zummit, have the acumen and the ability to work with various tools, technologies and frameworks for building ML models that are required for the project needs.

Zummit Areas of Expertise



 
Emotion detection   Time Series analysis
Racism and Terrorism detection are some of the key use cases around emotion detection. Uses Deep Neural Networks and architectures similar to AlexNet under Computer Vision.   Analysis of Time Series data is quite an interesting area, especially when dealing with use cases like Stock Market Predictions, Forex, Crypto and the like
     
 
Reinforcement Learning (RL)   Language Processing
A powerful approach for scenarios other than linear or ANN problems. A model that learns over time by observing data. Autonomous driving is one of the best examples of RL   Analysis of Time Series data is quite an interesting area, especially when dealing with use cases like Stock Market Predictions, Forex, Crypto and the like
     
 
Anomaly Detection   Generative Solutions
Fraud detection is one of the best examples of detection anomalous transactions in Banking and financial applications. Prescriptive analytics in ecommerce, healthcare disease data and more.   One of the most cutting edge use cases around Fashion domain like Hairstyle suggestions, apparel design, 3D models of house designs, Anime characters generation and more.
     
   
Data Analytics    
One of the key benefits or outcome of AI/ML solutions is the ability to provide analytics, be it, Preditive Analytics, Prescriptive Analytics or even Descriptive Analytics.    

Zummit Project Lifecycle
1. Understanding the Problem Statement
The first step is to clearly define the problem statement and scope.
For making it easy, we break down the project into tasks and UML use case diagrams 		 
     
2. Data Pre-processing & Feature engineering
The source data may come from various origins, like IoT devices, data feeds, API pulls, databases or even excel/csv files. In this phase of the Lifecycle, bad data, unwanted data, outliers, empty values are all removed. Feature engineering tasks like choosing the required columns, principal component analysis are performed 		 
     
3. Training the model
The source data may come from various origins, like IoT devices, data feeds, API pulls, databases or even excel/csv files. In this phase of the Lifecycle, bad data, unwanted data, outliers, empty values are all removed. Feature engineering tasks like choosing the required columns, principal component analysis are performed 		 
     
4. Validating the model
The source data may come from various origins, like IoT devices, data feeds, API pulls, databases or even excel/csv files. In this phase of the Lifecycle, bad data, unwanted data, outliers, empty values are all removed. Feature engineering tasks like choosing the required columns, principal component analysis are performed 		 
     
5. Deploy the model
The source data may come from various origins, like IoT devices, data feeds, API pulls, databases or even excel/csv files. In this phase of the Lifecycle, bad data, unwanted data, outliers, empty values are all removed. Feature engineering tasks like choosing the required columns, principal component analysis are performed 		 
     
6. Monitoring and fine tuning
The source data may come from various origins, like IoT devices, data feeds, API pulls, databases or even excel/csv files. In this phase of the Lifecycle, bad data, unwanted data, outliers, empty values are all removed. Feature engineering tasks like choosing the required columns, principal component analysis are performed 		 
     
 

Tools and technologies

Zummit Infolabs primarily works on
  • Python, Tensorflow, Keras, Pytorch, OpenCV
  • CNN, RNN, LSTM, GRU, Transformers
  • AlexNet, FaceNet, UNet, DLib, Yolo
  • AWS, Azure, UIPath
  • Django, React, NodeJS, Angular, Php, MySQL
  • Microservices, Gitlab, MVTU, MVC
 		 
 



Client Testimonials

Grabbngo, USA     


We engaged Zummit for Recommender application and ML based Forex Analysis and we are quite happy

Enable Skills, India  


Zummit worked with us on EduTech analytics use case. They have good ML and EduTech knowledge

Sprhava, Germany   


We are constantly working with Zummit on Computer Vision, Self-driving cars and complex use cases and very happy with them

Rec Services, UK   


Currently engaged with a Neural Network based unique Audio/Video analysis project and so far it has been great