Using Satellite Imagery + AI to predict the financial health of a company

Parking lot photo from Unsplash

To evaluate the financial health of a company, four main factors can be examined: liquidity, solvency, profitability and operating efficiency. Out of these 4 metrics, profitability is the best measurement of an institution's financial health. To survive in the long run, a company must attain and maintain profitability.

One of the key indicators of increasing profits is the number of active customers. For e.g., a brick and mortar store such as McDonald's would have high profitability and increased net margin (assuming operating costs remain the same) if more customers visit the store and buy its product. …


TensorFlow Dev Summit 2019

The 2019 edition of the TensorFlow dev summit got off to a great start on a rather cold and rainy morning in Sunnyvale, CA. This time around, the TensorFlow team has made certain visible changes from previous year’s edition:

  1. Summit is now a 2-day event with the first day full of talks and demos whereas the second day focussing on hands-on sessions where attendees can code along with the TensorFlow team members and engage them on the real-life problems they are solving.
  2. The event has a much larger invitee list and this is visible from their venue (Google Event Center)


In September of 2018, Microsoft launched Azure Machine Learning Service that helps data scientists and machine learning engineers build end-to-end machine learning pipelines in Azure without worrying too much about dev-ops behind training, testing and deploying your model. Azure has a number of offerings in this space such as Azure Notebooks, Azure Machine Learning Studio and Azure Batch AI. Comparison of the offerings is available here.

This article will focus on the newest offering by Azure, and it will cover the basic concepts including an example of training your own machine learning model. Following is the breakdown of the article:


This article is a comprehensive overview of using deep learning based object detection methods for aerial imagery via drones. We also present an actual use of drones to monitor construction progress of a housing project in Africa.

#update1: We just launched Nanonets Drone APIs!

Did you know Drones and it’s associated functions are set to be a $50 billion industry by 2023? As of today drones are being used in domains such as agriculture, construction, public safety and security to name a few while also rapidly being adopted by others. With deep-learning based computer vision now powering these drones, industry experts are predicting unprecedented use in previously unimaginable applications.

We explore some of these applications along with challenges in automation of drone-based monitoring through deep learning.

Finally, a case-study is presented for automating remote inspection of construction projects in Africa using Nanonets machine learning…


In the first part of this series, we discussed how to create a production-ready model in TensorFlow that is compatible with TensorFlow serving and in the second part we discussed how to create TF-serving environment using Docker. In this part, we will talk about creating a client that will request the model server running in the Docker container for inference on a test image.

A quick introduction to gRPC (Google Remote Procedure Call) and Protocol Buffers

gRPC (Google’s Remote Procedure Call) is google’s HTTP2 wrapped RPC protocol. What this allows is for a client running on a computer to access a remote computer, via a computer network, and call a “function” on…


Object detection models are some of the most sophisticated deep learning models. They’re capable of localizing and classifying objects in real time both in images and videos. But what good is a model if it cannot be used for production?

Thanks to the wonderful guys at TensorFlow, we have TensorFlow serving that is capable of serving our models in production. There are some really good articles on TensorFlow serving to get you started such as this one and this one.

This article will focus on how we can serve Object Detection Models specifically with TF Serving. It is motivated by…


In Part 1 of this series, I wrote about how we can create a production-ready model in TensorFlow that is compatible with TensorFlow serving. In this part, we will see how can we create TF-serving environment using Docker.

About Docker

Docker is a software tool that lets you package software into standardised units for development, shipment and deployment. Docker container image is a lightweight, stand-alone, executable package of a piece of software that includes everything needed to run it: code, runtime, system tools, system libraries, settings.

In short, Docker lets us you isolate your application and its dependencies in a stand-alone package…


Object detection models are some of the most sophisticated deep learning models capable of localizing and classifying objects in real time both in images and videos. But what good is a model if it cannot be used for production?!

Thanks to the wonderful guys at TensorFlow, we have TensorFlow serving that is capable of serving our models in production. Now there are some really good articles on TensorFlow serving to get you started such as this and this. This article will focus on how can we serve Object Detection Models specifically with TF Serving. It is motivated (in my opinion)…

Gaurav Kaila

Data Science Manager @EY and Chief Data Scientist @IdeaChain; A hub for ideas, discussion and collaboration -http://ideacha.in

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