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Services » Application Management Services » Artificial Intelligence And Machine Learning

Artificial Intelligence(AI) & Machine Learning(ML)


The Artificial Intelligence (AI) Market Size is projected to be around US$ 191 Billion by 2024.

The applications of AI and ML are manifold. From predicting customer behavior, and product recommendations, to enhancing market strategies, improving productivity, financial analysis, medical treatment and detecting frauds and network intrusions, the possibilities for the application of AI are endless.

AI simulates human intelligence but its capabilities extend beyond to perform activities which a human cannot realistically achieve given time, labor and budget constraints.

AI solutions harness Machine Learning, Deep Learning and Natural Language Processing capabilities. Data carried on neural networks are mined to extract insightful information using layers of algorithms. Deep learning principles dive into astronomically large pools of data to create language processing, speech recognition and image recognition capabilities.

AI technologies - Tools, Libraries and Platforms

  • Google AI
  • Tensor Flow
  • Theano
  • DeepLearning 4J
  • Torch
  • Caffe
  • IBMWatson
  • Microsoft CNTK

GSS ML Capabilities

  • Regression, Classification, Timeseries, Deep learning, NLP modelling.
  • LinearRegression, LogisticRegression, DecisionTree, Random forest, SVM, GBM, ARIMA, CNN, LSTM, NLTK algorithms.
  • Small datasets using scikit-learn.
  • Large datasets(upto 10GB) using PySpark, ml.

Why GSS?

GSS has successfully implemented and executed AI/ML projects with following results:
  • A timeseries/regression combined model we created, gave us predictions with 92% accuracy.
  • Our best scores for regression algorithms are around 0.9.
  • Our Deep Learning experiments have led us to achieve 90% accuracy levels using RNN(LSTM) multivariant timeseries based problem and 70% accuracy for our image recognition CNN experiment.
  • Projects done include
    • Tata Power generation prediction analysis, Retail sales and storage prediction analysis.
    • NYC fare amount prediction, Amazon reviews sentiments analysis using NLTK, Amazon sales prediction analysis.


The most popular and visible application of AI are Chatbots. Text based programs, chatbots interact with people using AI and NL.

Chatbots are used primarily in :
  • Content delivery of push, pull content, and to collect customer preferences.
  • For 24/7 availability for customer enquiries.
  • E-commerce sites for interaction on product line availability, pricing etc.
  • Hospitality industry for ticket booking for travel and stay.
  • Healthcare industry for standard responses for query resolution.
Tools used in Chatbot development
  • TARS
  • Botsify
  • Chat Fuel
  • Flow XO
  • ManyChat
  • BotKit

Deep Learning

Deep Learning is a subset of AI and ML. Drawing on NLP and Image and Speech recognition capabilities DL Applications are leveraged in areas such as
  • Product Demand predictions.
  • Healthcare care for medical diagnosis.
  • Healthcare segment for anomaly detections.
  • Detection of fraud.
  • Image recognition.

Image Recognition

Image recognition bestows the ability of a software to identify places, objects, people, and actions in images. It enables machine vision through convolutional neural networks. A powerful enabler in driverless driving, augmented reality applications, education and tourism, and security Image recognition is among the emerging and sought after technologies in IA.
Tools used
  • Talkwalker
  • IBM Watson Image Recognition
  • Google Lens
  • Amazon Rekognition
  • Clarifai
  • Cloudsight

Data Science

Advanced Data Science techniques extract meaningful information from data. Data science involves various techniques. Data preparation includes data explorations, cleaning, changing, shaping and finally publishing for Analysis.

This data is subjected to statistical modelling and algorithmic development. Analytical models on Big Data is created using statistical methodologies and/or in combination with ML.
  • Data preparation, management and harmonization.
  • Data exploration, cleaning, shaping and publishing for analysis.
  • Statistical Modelling.
  • Algorithm Development.

Robotic Process Automation (RPA)

RPA is a powerful digital transformation enabler.

The alliance between RPA and NLP and ML incorporates within products cognitive abilities. Bots capture data, manipulate and interpret the information to trigger responses while communicating with other digital systems.


  • Optimize processes and are not just cost savers.
  • Help seamless operations through automated processes.
  • Support scalability, flexibility, reliability and ease of use.
The best systems use algorithms bundled with advanced data mining techniques to create intelligent systems that not only make optimal decisions but also learn from the environment.
Tools used
  • Automation Anywhere.
  • Blue Prism.
  • Ui Path.
  • OpenSpan.
  • WorkFusion.
AI systems replicate human intelligences such as planning, learning, reasoning, problem solving, perception, sentiment understanding and even social intelligences and creativity.