Accelerate Your ML Journey with TensorIoT & AWS

Build Intelligent Systems with Amazon SageMaker.

Our business is a leader in conceptualizing, building and deploying cutting edge machine learning solutions in the cloud and on the edge. We work closely with your business team and data experts to translate AI and ML ideas to high-performing and scalable models that increase efficiency, save costs and build business value. Our machine learning division delivers end-to-end expertise across industries in computer vision, forecasting and time series analyses, anomaly detection/preventive maintenance, fraud detection and broad applications of predictive models.  Our strength is building models with quick turnaround for reliable deployment and ROI.

Our Machine Learning Solutions

Our pre-built models on AWS allow customers to rapidly validate outcomes and adopt these technologies in their businesses.



Improve service & efficiency.

  • Insurance Pricing.
  • Claim Fraud Prevention.
  • Claim Payment Automation.
  • Direct Marketing.

Customer Service

Customer Service

Improve customer experience.

  • Automated Responses.
  • Custom Chatbot with Q&A service.
  • Optimized Routing.
  • Re-design App Workflow.



Optimize your network.

  • Prioritize for Edge.
  • Monitoring infrastructure.
  • Enhance legacy infrastructure & devices.
  • Routing & Caching Optimization.



Reduce risks to portfolio.

  • Credit Scoring.
  • Fraud Prevention.
  • Risk Management.
  • Stress Testing.



Improve patient care.

  • Disease Propensity.
  • Forecasting ICU Occupancy.
  • Re-admission Risk.
  • Track Compliance.



Improve Outcomes.

  • Predict Customer Churn.
  • Customer Segmentation.
  • Lifetime Value.
  • Personalized Advertising.

Customer Success

Machine Learning, especially ML @ Edge, is in its infancy but its impact is already evident across a variety of industries.

Intelligent machines lead to significant cost savings for industrial manufactures 

Reliance Steel teamed up with TensorIoT to connect its industrial machines to the internet and get telemetry data from the different sensors and loggers on the machines. Reliance Steel uses Amazon SageMaker to do preventive maintenance on their industrial machines based on the data that’s being brought in to the system.

By partnering with TensorIoT to develop an Industrial IoT solution, Reliance Steel brought innovation to its machinery through the implementation of modern architectures, sensors for legacy augmentation, and machine learning to make the newly acquired data intelligent and actionable.

Safer Driving solution deployed on existing and new vehicles

Vodafone wants to leverage their LTE platform to deploy a driver monitoring solution at the edge to reduce accidents and promote safer roads. The solution had to be inexpensive and purchased off the shelf to be installed on new or existing vehicles.

The solution leverages AWS Greengrass, Machine Learning at the edge to perform inference. The camera feed is transmitted over the Vodafone’s LTE network from the car to offload the computational heavy lifting to Saguna’s multi-access edge compute platform.

AWS Machine Learning Offerings

Our ML offerings on AWS accelerate integration of machine learning in products and processes across your business.

Getting Started with AWS DeepLens –
3 Days

  • Rapid Device Setup

  • Use built-in models

  • Create custom models

  • Deploy models to AWS DeepLens

  • Intro to AWS Lambda

  • Intro to Amazon Sagemaker

  • Train with Amazon SageMaker

  • Automate end to end workflow

Amazon Sagemaker Labs –
1 Week

  • Intro to Amazon SageMaker

  • Notebooks in Amazon SageMaker

  • Deploy a trained motel

  • Bring your own model

  • Review built-in algorithms

  • Training a model

  • Invoking the endpoint at scale

  • Other frameworks in Amazon SageMaker

Machine Learning at EDGE Labs –
1 Week

  • Intro to AWS IoT and AWS Greengrass

  • Install & Setup AWS Greengrass

  • Deploy model to Edge

  • ML Inference on Edge

  • Intro to AWS Lambda

  • Create and train model

  • Review model versioning support

  • Automate discovery to notification

Ubiquitous Voice Interface with Alexa
– 1 Week Lab

  • Review Alexa, ASK, AVS and A4B services

  • Gather requirements and user stories

  • Create custom skill

  • Integrate with wider AWS ecosystem

  • Create a starter skill

  • Lead design and development of UX/UI

  • Assist with ROI analysis

  • Advanced Admin Panel

Product ML Implementations –

  • Map requirements to user stories

  • Lead design, development and testing

  • Automate the full CICD process

  • Source Files Included

  • Define a ML roadmap

  • Develop Models with Customer team

  • Advanced Admin Panel

  • 24×7 Priority Email Support

View our Events page to see more of our Machine Learning Offerings

View Upcoming Events

Machine Learning Technology

Machine Learning, especially ML @ Edge, is in its infancy but its impact is already evident across a variety of industries.

Anomaly Detection

Accurately predict outliers.

Applications range from intrusion detection (identifying strange patterns in network traffic that could signal a hack) to system health monitoring (spotting a malignant tumor in an MRI scan), and from fraud detection in credit card transactions to fault detection in operating environments.

Built using MXNet in Amazon Sagemaker, our domain specific supervised and unsupervised models may be retrained with your data.

Object Detection

Detect and Localize objects.

Fast and accurate systems for object detection and localization are necessary for autonomous vehicles, smart video surveillance, aerial image analysis, facial detection and various people counting applications.

SSD and Faster R-CNN based models built in Amazon Sagemaker to rapidly re-train using your specific data.

Click-Through Predictions

Improve customer experience and CTR.

Applications range from multiple classification, regression, and ranking use-cases. This may be added into real-time bidding systems or intergrated into the customer workflow on websites and apps.

The model uses XGBoost to run a real-time predictor and return a scored prediction result.