DevConf.IN'19 has ended
DevConf.IN is the annual Developer’s Conference organized by Red Hat, India. The event provides a platform to the FOSS community participants and enthusiasts to come together and engage in knowledge sharing activities through technical talks, workshops, panel discussions, hackathons and much more.

All you have to do is a FREE registration here. There is no admission or ticket charges applicable to be a part of the event. All the event activities will be in English.

We are committed to foster an open and welcoming environment for everyone at our conference. We have our inclusive code of conduct and media policies, which we expect to be duly adhered during the event.

When: August 2-3, 2019

Venue: Christ University - Bengaluru, India

Click here for more updates about DevConf.

Registration Link: https://devconfin19.eventbrite.com

Last Date of Registration: 31st July, 2019

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AI / ML [clear filter]
Friday, August 2


The Practical Effectiveness of Deep Learning
With the advent of better compute and big data, enterprises are looking at leveraging the latest and the best techniques in artificial intelligence, or to be more specific deep learning. The effectiveness of deep neural networks has been widespread in solving diverse problems across the industry, thanks to open-source tools like tensorflow & keras.

We'll cover deep learning basics, deep neural network architectures, popular open-source tools and talk about some real-world case studies in the industry which are being solved using deep learning. This is not intended to be a theoretical lecture about neural networks but more of a practical session talking about applied deep learning techniques and case-studies in the industry.

Session Outline:
  1. Introduction
  2. Deep Learning Basics
  3. Deep Learning Effectiveness
  4. Deep Learning Frameworks
  5. Deep Learning Model Architectures
  6. Case Studies

Case Studies:
  • Predicting Data Center Device Failures
  • Pro-active Incident Resolution
  • Malaria Detection (includes hands-on code walkthrough)
  • Pro-active Security Vulnerability Detection for golang-Kubernetes-OpenShift eco-system (includes hands-on code walkthrough)

We will showcase the hands-on code walkthrough using Jupyter notebooks which you can re-use from my GitHub in the future. Code will be in tensorflow \ keras.

avatar for Dipanjan Sarkar

Dipanjan Sarkar

Data Scientist, Red Hat
Dipanjan (DJ) Sarkar is a Data Scientist at Red Hat, a published author and a consultant & trainer. He has consulted & worked with several startups and Fortune 500 companies. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent... Read More →


Low-latency multi-label text classifier using BERT
This session we will review Google BERT - a tried and tested, as well as State of The Art technique for NLP tasks. We will go through the text encoding process which is similar to word-vectors approach. We will also explore the WordPiece tokenizer and see how that covers much of English language words. Then we will bulid a Multi-Laber text classifier using the encodings. Once we have the basic framework ready we will train the models and see how it performs. Next, we will go through key-steps to optimze the resulting model(s) for serving. Finally we will see a demo of the classifier we just built that runs quite fast. All the model training code will be in Python and some parts of model serving will be in Java.


avatar for Saleem Ansari Ansari

Saleem Ansari Ansari

Sr. Machine Learning Engineer, Selerity Inc.
Software Engineer, with 12 years of experience in Software Architecture, Design and Development with Machine Learning, Information Retrieval, and Web Application technologies.​https://github.com/tuxdna... Read More →


Building Open Distro for Elasticsearch
Whenever you think of implementing search for your application data, Elasticsearch bubbles up as one of the top choices for search. Elasticsearch is a popular, open source search stack used by web, mobile and cloud applications for search applications. The Elasticsearch stack (ELK) is built on key open source components such as Apache Lucene, Jackson, Netty.

Open Distro for Elasticsearch is an open source distribution released by AWS earlier this year. This distribution bundles several open source components including a performance analyzer, performance agent, alerting and SQL features to monitor Elasticsearch. The talk will do a deep dive of the features and build tools for Open Distro for Elasticsearch. It will also cover the project's governance and collaboration tenets.

avatar for Alolita Sharma

Alolita Sharma

Principal Technologist, AWS
Alolita Sharma is a Principal Technologist at AWS. Currently, she drives open source strategy and developer contributions for open source projects such as Open Distro for Elasticsearch. Two decades of doing open source continue to inspire her. Alolita has built and led engineering... Read More →


Conversational AI and Virtual Assistants
"Conversational AI is a new emerging field in the Artificial Intelligence area which enables humans to interact with machines using Natural Language. This enables people not skilled in IT to interact with machines easily and extends the reach of technology to a broader community.
This session will take through the use of Conversational AI for Virtual Personal Assistant in other words Virtual Agents for Service Desk. The session will cover the architecture of Learning Understanding Cognitive Assistants and a short demonstration of the same. "

avatar for Saravanan Devendran

Saravanan Devendran

Chief Architect, IBM
Saravanan Devendran is passionate about innovation, learning new technologies and key leader in Cognitive Automation. He worked in various positions within IBM for 21+ years. He is currently the STSM & Chief Architect focusing on Cognitive Assistant Technology for IT Services.

Aditya Burli

Product Manager, IBM
Adi is a passionate Software Professional with over 15 years of professional IT experience spanning Product Development and Service Delivery. He has extensive knowledge in Integration domain, Cloud, Cognitive Computing and DevOps methodology.


Openshift 4 & Machine Learning with KubeFlow
The agenda of this presentation is to demostrate Machine Learning with our Hybrid Cloud Infrastructure Red Hat Openshift .

Infrastructure engineers will often spend time modifying deployments before a single model can be tested. These deployments are often bound to the clusters they have been deployed to, thus moving a model from a laptop to a cloud cluster is difficult without significant re-architecture.
The open source Kubeflow project addresses these concerns by enabling Github Machine Learning stacks on Kubernetes portable across environments.
This presentation will focus upon the ML model which consists the following components.
o Jupyter Hub
o Tensorflow Custom Resource (CRD) that can be configured to use CPUs or GPUs, and adjusted to the size of a cluster
o TF Serving container

avatar for Rushil Sharma

Rushil Sharma

Technical Support Engineer, Red Hat
Hi, I am a gamer, Love Hollywood and waiting for robots to become self-aware. Red Hat has always been my dream company and I have always been very intrigued by technology and started off by learning Linux pretty early.
avatar for Neeraj Bhatt

Neeraj Bhatt

Technical Support Engineer, Red Hat
I am a technology enthusiast, Love to explore new tech and implement it. ML is one of my fav techs of today and integrating it with openshift is really amazing.


Panel Discussion:- The Promise and Peril of Artificial Intelligence: Is AI a bane or boon for our society?
There have been two worlds w.r.t the outlook on AI and AGI with the media. On one hand portraying a grim future around killer robots, rise of the machines and the doubt of having intelligence govern or take critical decisions when we can’t even explain sometimes how it works. On the other hand, with the advent of newer tools, techniques, hardware and methodologies in ML and DL we have seen remarkable advancements in areas pertaining to technology, healthcare, agriculture, logistics, transportation and a multitude of diverse domains impacted positively by AI. The intent of this panel is to discuss their viewpoints on how they think AI can be a ‘bane’ and\or a ‘boon’ to society and what should we be looking forward to (promises) and be careful about (perils) in the 21st century!
Moderator: Dipanjan Sarkar

avatar for Lavanya Tekumalla

Lavanya Tekumalla

Founder, AiFonic Labs
Lavanya Tekumalla is the founder of AiFonic Labs, prior to which she was a machine learning scientist at Amazon. She holds a PhD in Machine Learning from the Indian Institute of Science.  She has worked in the industry for about 9 years in various roles (at Amazon, InMobi, Myntra... Read More →
avatar for Hemant Misra

Hemant Misra

Vice President - Head of Applied Research, Swiggy
Hemant Misra has been an active researcher in the broad areas of text and signal processing, speech/speaker recognition, information retrieval, machine learning, healthcare applications and education. After finishing his M.S. thesis in 1999 from IIT, Madras, on the topic of speaker... Read More →
avatar for Vijay Agneeswaran

Vijay Agneeswaran

Director of Data Sciences, Walmart Global Technology Services Private Limited
Dr. Vijay Srinivas Agneeswaran has a Bachelor’s degree in Computer Science & Engineering from SVCE, Madras University (1998), an MS (By Research) from IIT Madras in 2001, a PhD from IIT Madras (2008) and a post-doctoral research fellowship in the LSIR Labs, Swiss Federal Institute... Read More →
avatar for Saptarshi Das

Saptarshi Das

Manager - Data Science R&D, Shell
Dr. Saptarshi Das (PhD) brings leadership, business, and technical expertise to achieve business objectives. He is known for innovating, developing and deploying data-driven products.

Saturday, August 3


Genetic Algorithm Optimization for Deep Learning
Selection of the optimal parameters for Machine Learning tasks is challenging. Creating a perfect deep learning model requires a hefty amount of hyperparameter tuning, which involves brute force trial and error, and takes an immense amount of time. In this session, we improve the brute force method using Genetic Algorithm(GA) to achieve a network with the optimal hyperparameters. GA is commonly used for generating high-quality solutions for optimization using bio-inspired operators such as mutation, crossover, etc. For Neural Networks, weights in all layers help to achieve high accuracy. So each solution given by Genetic Algorithm holds all parameters that might help to enhance the results.


Lucky Suman

Associate Software Engineer, Red Hat
Myself Lucky Suman, giving my services as an Associate Software Engineer at Red Hat from last 8 months. Have keen interest in Machine Learning, Nature Inspired Algorithms and NLP.


Personalized learning with bio signal maps and ML
Students who wont get it for first time are not likely to get it for next several times if they look at same content. Research says when a learner faces difficulty in understanding a piece of content, develops phobia about that topic or course and tends to loose interest.

The future reality of education will be more bite-sized and personalized experience crafted from finest content suiting to each person needs. In this session we will explore how trending techs like AI and ML are enabling smart way of offering personalized learning experiences to students using bio-signal data, via learning how student brain reacts for given piece of content, thereby calibrating bio-signals. This enables pre-processing of digital content just-in time before presenting to user, thus reducing brain load.

avatar for Geetha Karna

Geetha Karna

QA Lead, IBM ISL Private ltd
Geetha works for IBM ISL as QA Lead Engineer,Performance & capacity Management .Her Idea got published for QSE 4th Latin America Symposium, she published developer works articles . she holds 4 File Rated valuable IP’s. Geetha is passionate about coaching and Mentoring students for... Read More →


Effective integration of DevOps with MI & AI
Everyone now is interested in ML and AI. How we can use/integrate different DevOps tools with some tools and concepts from ML and AI. So that we can easily manipulate and present the data with even great efficient storage. I will talk for effective integration of IMacros, testim, pandas, seaborn, WTRobot (An web automation framework inspired from behave and robot framework.This framework would give you more customization and easy to use options.) with different DevOps tools like configuration and management, monitoring tools, databases, ELK stack and other tools. I will be providing usecases, scenarios, small demos. Also will differentiate different tools as usecases.

avatar for Anandprakash Tandale

Anandprakash Tandale

openstack engineer, Red Hat
Working in Red Hat, since last 3 years. Have good knowledge of Cloud, DevOps and ML and AI. Presented talks in many opensource meetups also including DevConf.Cz 18, OpenStack user group meetups, python Pune meetup, all hands. Enthusiastic to spread knowledge of opensource.


AI based application Insights
Develop more secure applications by leveraging security insights right inside IDE served by “Dependency Analytics”, an extension for VSCode and Eclipse CHE developed by the Red Hat Developer group. It deep dives into your application dependencies using AI. Thus, empowers you to make your stack complete by bringing visibility into any Security and License risks. Dependency Analytics has below features:
1. Flags security vulnerabilities (CVEs) and suggests a remedial version - including transitive dependencies
2. Suggests a project level license, check for conflicts between dependency licences
3. Shows Github popularity metrics of dependencies along with latest version
4. AI based guidance to suggest alternative and complementing dependencies

avatar for Siva Adhikarla

Siva Adhikarla

Principal Engineer, Red Hat
Engineering Manager @Red hat
avatar for Srikrishna Paparaju

Srikrishna Paparaju

Senior Principal Software Engineer, Red Hat


The Subtle Art of Backward Differencing
With lots of data being generated at a pace that was once unimaginable it's now a challenge to acquire, examine and process the data. This talk exposes the use of a simple mathematical technique that aids in finding patterns in data especially sensor data from various devices around us. Finding patterns in data is an important task for data scientists/machine learning engineers so as to train models for future classification/regression tasks. Backward differencing has other advantages in data cleaning and feature engineering.

avatar for Saketha Ramanujam Samavedam S

Saketha Ramanujam Samavedam S

Software Engineering Intern, rorodata
A final year undergraduate student at the GVPCE, Vizag(Graduating this summer). Research intern at GVP-SIRC working on applying Random Forest Classifier to photonic sensor data. A open source enthusiast with experience in Deep Learning and Computer Vision​https://github.com/sak... Read More →


Panel Discussion:- The AI Revolution: Gateway to success in your enterprise and career with AI
The focus of this panel is manifold, the idea here is with a new advent of the revolution being brought about in our life, career, and jobs with AI, how do we ensure success in our ever-evolving careers and also ensure success for the enterprises, companies we work in and the businesses we run. There will be three major themes for this panel:
  1. Enabling Successful Adoption of AI in your organization: What does it really take to move from a data-driven company to be an AI-driven company? How do we tackle problems AI\Data first? What kind of workflows and processes are followed to take an AI product or solution from conception to production? How much importance should be given to understanding business problems, do we notice any gaps between the business and data scientists\engineers. How important is engineering to ensure the success of an AI product? How do we monitor the performance of AI models after production? How do we make our clients, business, C-level execs embrace change in existing processes with AI

  2. Job Roles in Data Science\AI and what they do: The ever-changing job landscape has been hit with a multitude of roles pertaining to data science and AI. We have data scientists, data engineers, ML engineers, DL engineers, data analysts, business analysts, ML architect and many more. What are some of the major job roles or rather tasks which people have to do in this field? What is a typical day in the life of a data scientist\manager? How do we measure and ensure success in these new job titles? What are some of the key use-cases panelists might have solved to drive their own careers towards success? Are creativity and innovation often dying out in larger companies which suffocate data scientists? etc
  3. How to build a successful career in data science: I’m a student, there is literally an overload of information out there with regard to data science resources, bootcamps, trainings, lectures, courses, degrees. Almost everyone seems to be “doing data science”. What do I need to learn, practice, implement and work towards so I can break into this field. What are the key pitfalls and challenges I should be careful about? How do I make sure I don’t get too deep and disillusioned into the AI hype?
Moderator: Maheshwari Dhandapani

avatar for Abhishek Kumar

Abhishek Kumar

Senior Manager Data Science, Publicis Sapient
Abhishek Kumar is a senior manager of data science in Sapient’s Bangalore office, where he looks after scaling up the data science practice by applying machine learning and deep learning techniques to domains such as retail, ecommerce, marketing, and operations. Abhishek is an experienced... Read More →
avatar for Shalini Sinha

Shalini Sinha

Director - Data Science, Numerify
Shalini Sinha has 18 years of industry experience majority of which is spent in analytics product development. Currently she is working at Numerify as Director of Data Science and prior to this she lead CRM analytics team in Oracle. Numerify is a Cupertino head-quartered startup that... Read More →
avatar for Hima Patel

Hima Patel

Research Manager, IBM Research
Hima Patel is a research manager at IBM Research Lab and leads a team of researchers that work in problems at the intersection of machine learning and NLP. Her passion is to work on research areas that help solve real business problems. Prior to IBM, she has spent time in research... Read More →
avatar for Seema Chopra

Seema Chopra

Global Technical Leader, Boeing India
Seema is working as Global Technical Leader - Data Analytics at Boeing Research and Technology, India. She has recognised as the part of Boeing Technical Fellowship and became Boeing India’s first ‘Associate Technical Fellow’ for Artificial Intelligence. Her current work includes... Read More →