Nougat: Neural Optical Understanding for Academic Documents

October 3, 2023


In the digital age, the sheer volume of academic documents available online has grown exponentially. Researchers, students, and scholars often find themselves navigating vast collections of PDFs, struggling to extract information efficiently. That's where Nougat, an innovative system developed by Meta, comes into play. Nougat leverages the power of Neural Optical Understanding to revolutionize the way we interact with academic documents.

Understanding Nougat

Nougat is not your typical document processing tool. It's a sophisticated system built upon the foundation of cutting-edge machine learning techniques, particularly the Document Understanding Transformer (Donut) architecture. Donut combines the strengths of neural networks and transformers to achieve remarkable results in parsing academic documents.

Key Features of Nougat

  • Multi-Modal Understanding: Nougat goes beyond traditional text extraction by integrating visual content analysis. It can recognize and interpret not only text but also images, equations, and tables within academic papers.
  • Extensive Training Data: To train Nougat effectively, the Meta team compiled a massive dataset of over 8 million articles from sources like arXiv, PubMed Central, and industry documents libraries. This extensive training data empowers Nougat to handle a wide range of academic documents.
  • Flexible Output: Nougat outputs the information it extracts from PDFs into a Multi-Markdown file format. This versatile output can be easily integrated into various research workflows and platforms.

Using Nougat

Getting started with Nougat is straightforward, thanks to its user-friendly interface. Researchers and academics can apply Nougat's Optical Character Recognition (OCR) capabilities on their academic documents, enabling them to extract, understand, and work with the content more effectively.

  • Batch Processing: Nougat supports batch processing, making it convenient to analyze multiple documents simultaneously. This feature is particularly useful for researchers working with large datasets.
  • Image and Text Integration: Nougat seamlessly integrates text and visual content, making it a valuable tool for disciplines where equations, graphs, and images play a crucial role.
  • Latex Compatibility: For those in the academic community using LaTeX for document preparation, Nougat's Multi-Markdown output is compatible with LaTeX, ensuring smooth integration into research papers and publications.

Impact on Academic Research

Nougat has the potential to significantly impact academic research in several ways:

  • Time Efficiency: By automating the process of content extraction and understanding, Nougat frees up researchers' time, allowing them to focus on higher-level analysis and interpretation.
  • Interdisciplinary Research: Nougat's ability to handle both text and visual content encourages interdisciplinary research, where collaboration across diverse fields becomes more accessible.
  • Accessibility: The easy-to-use interface of Nougat makes academic documents more accessible to a broader audience, including students, researchers, and educators.

Nougat Workflow

This architectural diagram represents the following components and their interactions:

  • Academic Document: Represents the input academic paper in PDF format.
  • PDF Processor: This component handles the initial processing of the PDF document, including text extraction and image processing.
  • NLP Engine: Stands for Natural Language Processing Engine, responsible for understanding and extracting textual information from the academic document.
  • Visual Encoder: Handles the visual content of the document, such as images and equations. It provides image embeddings for further processing.
  • Transformer Decoder: Utilizes the Transformer architecture to decode both text and visual information, facilitating a cross-modal understanding of the document.
  • Multi-Markdown Converter: Converts the decoded content into multi-markdown format, making it suitable for various applications, including rendering in markdown or LaTeX.

This diagram offers a high-level view of how Nougat processes academic documents, incorporating both textual and visual elements to achieve optical understanding.

Please note that this is a simplified representation, and the actual Nougat architecture may involve more complex components and interactions. The diagram can be further extended to include details specific to the Nougat system's implementation and additional components

Flow of Image Augmentation in Nougat

The above given flow shows the different image augmentation methods used during training the model. A more detailed flow is shown in this paper with a sample document example.

Hands-On Examples on Using Nougat

  1. Tutorial 1


This tutorial explores the practical application of Meta's Nougat model for Optical Character Recognition (OCR) on academic and scientific papers. Nougat is an advanced neural network model tailored to efficiently parse PDF documents, extract text, mathematical equations, and tables. This comprehensive guide will walk you through essential aspects of using Nougat, from initial setup to OCR processes, batch processing, and additional learning resources.

Table of Contents

  1. Overview of Nougat
  2. Environment Setup
  3. OCR of PDFs
  • Natively Digital PDFs
  • Scanned PDFs
  1. Batch Processing
  2. OCR of Natively Digital PDFs: Unveiling Precision in Equation Recognition while comparing with LaTex

1. Overview of Nougat 

Nougat is an encoder-decoder Transformer model based on the Document Understanding Transformer (Donut) architecture. It is specifically designed for handling complex academic documents. Key functionalities of Nougat include:

  • Parsing PDF documents.
  • Extracting textual content, mathematical equations, and tabular data.
  • Utilizing a visual encoder for image processing.
  • Decoding content into token sequences through a Transformer decoder.

The model's extensive training on a diverse dataset of over 8 million articles from sources like Archive, PubMed Central, and the Industry Documents Library ensures its adaptability to various academic documents.

2. Environment Setup 

Before diving into Nougat's OCR capabilities, it's crucial to set up the environment for smooth execution. Follow these steps:

  • Configure the runtime environment to use a GPU.
  • Install the necessary modules, including Nougat, IPython, and Os.

from IPython import display
import os

!pip install git+

For getting all the commands and information from the command line, you can refer to the below shown image:

!nougat -h

The output after running the command is:

3. OCR of PDFs 

Nougat excels in OCR processes for academic documents, whether they are natively digital PDFs or scanned documents.

3.1. OCR of Natively Digital PDFs 

Natively digital PDFs are those already in digital format, simplifying the OCR process:

  • Download the target PDF using the curl command or any preferred method.
  • Execute Nougat to OCR the PDF and save the output in multi-markdown format.
  • Display the extracted content using markdown or render it in LaTeX, e.g., with Overleaf for further formatting.
  • Steps in code are as follows:

!curl -o quantum_physics.pdf

!nougat --markdown pdf '/content/quantum_physics.pdf' --out 'physics'

Please note: The below shown command is used to view a LaTex formatted file.


3.2. OCR of Scanned PDFs 

Scanned PDFs are essentially images of printed or handwritten documents, requiring OCR for text extraction:

  • Download the scanned PDF using curl or a suitable method.
  • Employ Nougat to perform OCR on the scanned document.
  • Post-processing may be necessary for formatting equations and titles when rendering in LaTeX or other tools.
  • Steps in code are as follows:

!curl -o fundamental_quantum_equations.pdf
!nougat --markdown pdf '/content/fundamental_quantum_equations.pdf' --out 'physics'

Please note: The below shown command is used to view a LaTex formatted file on E2E itself.


4. Batch Processing 

Nougat facilitates the efficient processing of multiple PDFs simultaneously, enhancing productivity. Here's how to batch process PDFs:

  • Create a directory to store the PDFs intended for processing.
  • Iterate through the PDFs in the directory using Python's os module.
  • Apply Nougat to each PDF in the batch, saving the results in multi-markdown format.
  • Steps in code are as follows:

!mkdir pdfs
!curl -o pdfs/lec_1.pdf -o pdfs/lec_2.pdf
!curl -o pdfs/lec_3.pdf  -o pdfs/lec_4.pdf

nougat_cmd = "nougat --markdown --out 'batch_directory'"
pdf_path = '/content/pdfs'
for pdf in os.listdir(pdf_path):
  os.system(f"{nougat_cmd} pdf /content/pdfs/{pdf}")

Please note: The below shown command is used to view the markdown file in the colab itself.


5. OCR of Natively Digital PDFs: Unveiling Precision in Equation Recognition While Comparing with LaTex

Below shown comparison is only for the 3.1 section, i.e, OCR of Natively Digital PDFs. Here, as per my observations, there are some misplacements in the title compared to the original pdf but the OCR has done a good job whilst playing with equations. 

  1. Tutorial 2


This tutorial uses Gradio as an interface to showcase the output of the Nougat model.

Table of Contents

  1. Installation
  2. Downloading a Sample PDF
  3. Downloading Model Weights
  4. Writing Inference Functions for Gradio App
  5. Building a Gradio Interface UI
  6. Conclusion

1. Installation

Before we begin, we need to install the necessary libraries, including Gradio and NOUGAT-OCR. Execute the following commands in your Jupyter Notebook or preferred Python environment:

!pip install gradio -U -q
import gradio as gr
!pip install nougat-ocr -q

2. Downloading a Sample PDF

In this tutorial, we will use a sample PDF for demonstration. You can also apply NOUGAT-OCR to your own PDFs. To download the sample PDF, execute the following code:

# Download a sample pdf file - (nougat paper)
import requests
import os

# create a new input directory for pdf downloads
if not os.path.exists("input"):
def get_pdf(pdf_link):

  # Send a GET request to the PDF link
  response = requests.get(pdf_link)

  if response.status_code == 200:
      # Save the PDF content to a local file
      with open("input/nougat.pdf", 'wb') as pdf_file:
      print("PDF downloaded successfully.")
      print("Failed to download the PDF.")


3. Downloading Model Weights

from nougat.utils.checkpoint import get_checkpoint
CHECKPOINT = get_checkpoint('nougat')

4. Writing Inference Functions for Gradio App

This code provides functions to download PDFs from given links, run NOUGAT-OCR on PDFs, and process PDFs into markdown content. It also includes CSS styling for a Gradio app's markdown display. These functions enable users to convert PDFs to markdown using the Gradio app.

import subprocess
import uuid
import requests
import re

# Download pdf from a given link
def get_pdf(pdf_link):
  # Generate a unique filename
  unique_filename = f"input/downloaded_paper_{uuid.uuid4().hex}.pdf"

  # Send a GET request to the PDF link
  response = requests.get(pdf_link)

  if response.status_code == 200:
      # Save the PDF content to a local file
      with open(unique_filename, 'wb') as pdf_file:
      print("PDF downloaded successfully.")
      print("Failed to download the PDF.")
  return unique_filename

# Run nougat on the pdf file
def nougat_ocr(file_name):

  # Command to run
  cli_command = [
      '--out', 'output',
      'pdf', file_name,
      '--checkpoint', CHECKPOINT,

  # Run the command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)


# predict function / driver function
def paper_read(pdf_file, pdf_link):
  if pdf_file is None:
    if pdf_link == '':
      print("No file is uploaded and No link is provided")
      return "No data provided. Upload a pdf file or provide a pdf link and try again!"
      file_name = get_pdf(pdf_link)
    file_name =


  # Open the file for reading
  file_name = file_name.split('/')[-1][:-4]
  with open(f'output/{file_name}.mmd', 'r') as file:
      content =

  return content

# Handling examples in Gradio app
def process_example(pdf_file,pdf_link):
  ocr_content = paper_read(pdf_file,pdf_link)
  return gr.update(value=ocr_content)

# fixing the size of markdown component in gradio app
css = """
  #mkd {
    height: 500px;
    overflow: auto;
    border: 1px solid #ccc;

5. Building a Gradio Interface UI

This code sets up an interactive interface using the Gradio library for running the NOUGAT-OCR tool. Users can upload a PDF or provide a PDF link. When they click the "Run NOUGAT🍫" button, the OCR process is triggered, and the converted content is displayed in the interface. Users can also clear the interface with the "Clear🚿" button. It's a user-friendly way to use NOUGAT-OCR for PDF conversion.

# Gradio Blocks
with gr.Blocks(css =css) as demo:
  with gr.Row():
    mkd = gr.Markdown('

Upload a PDF

',scale=1) mkd = gr.Markdown('


',scale=1) mkd = gr.Markdown('

Provide a PDF link

',scale=1) with gr.Row(equal_height=True): pdf_file = gr.File(label='PDF📃', file_count='single', scale=1) pdf_link = gr.Textbox(placeholder='Enter an arxiv link here', label='PDF link🔗🌐', scale=1) with gr.Row(): btn = gr.Button('Run NOUGAT🍫') clr = gr.Button('Clear🚿') output_headline = gr.Markdown("

PDF converted into markup language through Nougat-OCR👇:

") parsed_output = gr.Markdown(r'OCR Output📃🔤',elem_id='mkd', scale=1, latex_delimiters=[{ "left": r"\(", "right": r"\)", "display": False },{ "left": r"\[", "right": r"\]", "display": True }]), [pdf_file, pdf_link], parsed_output ) : (gr.update(value=None), gr.update(value=None), gr.update(value=None)), [], [pdf_file, pdf_link, parsed_output] ) # gr.Examples( # [["nougat.pdf", ""], [None, ""]], # inputs = [pdf_file, pdf_link], # outputs = parsed_output, # fn=process_example, # cache_examples=True, # label='Click on any examples below to get Nougat OCR results quickly:' # ) demo.queue() demo.launch(share=True)

Before adding any link: 

After completing the task:

6. Conclusion

In this tutorial, we learnt how to install and use NOUGAT-OCR to convert academic PDFs into a readable markup language and created an interface using Gradio.


The Nougat system represents a groundbreaking advancement in the realm of academic document processing. Its neural optical understanding capabilities, extensive training data, and user-friendly interface make it a valuable tool for researchers across disciplines. With Nougat, the task of working with academic papers becomes more efficient, opening up new possibilities for research and discovery.

As the academic landscape continues to evolve, Nougat stands as a testament to the potential of machine learning and artificial intelligence in transforming the way we interact with knowledge. Whether you're a seasoned researcher or a student embarking on your academic journey, Nougat is a tool worth exploring. It has the power to enhance your research capabilities and expand the horizons of academic discovery.

References and Further Learning 

To delve deeper into Nougat's technical details and to access additional resources, refer to the following:

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure