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Global Testing Retreat 2019Global Testing Retreat 2019
  • About
  • Presentations
  • Event Photos
  • Videos
  • Speakers
  • Schedule
  • Team
    • Jury & Advisory
    • Volunteers
  • Competition
    • #AutomATAhon2019
    • CPSAT Challenge
  • Leaderboard
  • Sponsor
  • Venue
  • Past Events
    • GTR2018
    • GTR2017
    • GTR2016

Presentations

Brijesh_Deb_Presentation

#ATAGTR2019 Presentation ``IoT applications pose a threat to verification and validation...My boss thinks otherwise!`` By Brijesh Deb

Avishkar_Nikale_Pressentation

#ATAGTR2019 Presentation ``DevSecOps with GitLab`` By Avishkar Nikale

Pallavi_Shetty_Anjali_Sharma_Presentation

#ATAGTR2019 Presentation ``Re-engineering perfmance strategy of deep learning applications using TensorFlow – a powerful AI package`` By Pallavi Shetty and Anjali Sharma

Nilanjan_and_Sibsankar_Presentation

#ATAGTR2019 Presentation ``Re-imagining Quality Assurance in the age of Digitization: With Customer Centricity at the Core`` By Nilanjan Chanda and Sibsankar Majumder

Sarah_and_RajKaliappan_Presentation

#ATAGTR2019 Presentation ``Performance testing of Chatbot`` By Sarah Lovely and Raja RajKaliappan

Suneeta_and_Sachin_and_Niharika_Presentation

#ATAGTR2019 Presentation ``QA alignment in the DevOps world`` By Suneeta Palande, Sachin Sangle and Niharika Dutta

Jibendu_and_Arpan_Presentation

#ATAGTR2019 Presentation ``Swallowables – The Next QA Frontier?`` By Jibendu Narayan Mazumder and Arpan Sarkar

Sai_Subramanian_Presentation

#ATAGTR2019 Presentation ``What’s your Cloud Assurance Strategy?`` By Sai Subramanian

Supriya_and_Nilesh_Presentation

#ATAGTR2019 Presentation ``Digital Assurance for Connected World`` By Supriya Bhosale and Nilesh Kherdikar

Supreetha_and_Rajneesh_Presentation

#ATAGTR2019 Presentation ``Curtailing Automation Impediments: Refactoring Automation Framework Architecture by Leveraging design patterns and dependency injection`` By Supreetha Keminje and Rajneesh Goyal

Pankaj_Kumar_Presentation

#ATAGTR2019 Presentation ``Security testing using ML(Machine learning), AI(Artifical intelligence), Deep learning(DL)`` By Pankaj Kumar

Prashant_Beniwal_Presentation

#ATAGTR2019 Presentation ``Role of QA in world of DevOps`` By Prashant Beniwal

Anish_Anand_Jha_Presentation

#ATAGTR2019 Presentation ``iOS App Automation, GitHub and Jenkins integration`` By Anish Anand Jha

Rutvikkumar_Mrug_Presentation

#ATAGTR2019 Presentation ``Blockchain and Continuous Testing`` By Rutvikkumar Mrug

Snehasish_and_Rekha_and_Umashankar_Presentation

#ATAGTR2019 Presentation ``AIML Driven extensive reusable Automation Asset Management Process (AIM) for ``QA/QE/Testing Org```` By Snehasish Roy, Rekha Shetty and Umashankar Reddy

Sundaresan_Krishnaswami_Presentation

#ATAGTR2019 Presentation ``Deliver with confidence using Consumer-Driven Contracts`` By Sundaresan Krishnaswami

Sijo_and_Bulu_Presentation

#ATAGTR2019 Presentation ``Delivering Cx through Continuous Performance Assurance using ElasticSearch etc.`` By Sijo Joy and Bulu Sahu

Himani_and_Brijesh_Presentation

#ATAGTR2019 Presentation ``How analytics is reshaping testing?!`` By Himani Yadav and Brijesh Deb

Brijesh_Prabhakar_Presentation

#ATAGTR2019 Presentation ``AI in App Dev & Test`` By Brijesh Prabhakar

Jitendra_Dutt_Sharma_Presentation

#ATAGTR2019 Presentation ``QE Organization strategy in Digital Era`` By Jitendra Dutt Sharma

Masa_Kevin_Maeda_Presentation

#ATAGTR2019 Presentation ``Improving quality efficiency and efficacy with XSCALE`` By Masa Kevin Maeda

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Modern information technologies and the advent of machines powered by Artificial Intelligence (AI) have strongly influenced the world. The industry must evaluate what it will take to support AI as it moves towards mainstream. The rapid evolution of algorithms, software, and hardware to support AI requires a framework to understand the challenges and create a context to meet those challenges. In recent times there has been increase in usage of AI based applications built using Tensor Flow package, which is a widely used library, by developers to build deep learning applications, it is necessary to construct a performance engineering methodology and approach to test and optimize deep learning applications using tensor flow package. This paper includes the performance engineering approach and performance optimization of Deep learning applications using Tensor Flow Package.