Nov 2019
18 Mon
19 Tue
20 Wed
21 Thu
22 Fri
23 Sat 08:30 AM – 05:30 PM IST
24 Sun
##URL for workshop date, time, venue, schedule and tickets: https://hasgeek.com/anthillinside/testing-machine-learning-applications-workshop/
Rapid progress in Machine Learning (ML) has seen a swift translation to real world commercial deployment. While research and development of ML applications have progressed at an exponential pace, the required software engineering process for ML applications and the corresponding eco-system of testing and quality assurance tools which enable software reliable, trustworthy and safe and easy to deploy, have sadly lagged behind. Specifically, the challenges and gaps in quality assurance (QA) and testing of AI applications have largely remained unaddressed contributing to a poor translation rate of ML applications from research to real world [107]. Unlike traditional software, which has a well-defined software testing methodology, ML applications have largely taken an ad-hoc approach to testing. ML researchers and practitioners either fall back to traditional software testing approaches, which are inadequate for this domain, due to its inherent probabilistic and data dependent nature, or rely largely on non-rigorous self-defined quality assurance methodologies. These issues have driven the ML and Software Engineering research communities to develop of newer tools and techniques designed specifically for ML. These research advances need to be publicized and practiced in real world in ML development and deployment for enabling successful translation of ML from research prototypes to real world. This tutorial intends to address this need.
This tutorial aims to
Target audience for this tutorial would include the data science and machine learning community folks. This would include
*** A basic degree of familiarity in ML concepts as well as basic/intermediate experience in developing of ML applications is expected from this tutorial audience.*** Audience should be familiar with the general software development life cycle as well as intermediate coding ability in one of the high-level programming language such as Python/R/Java/C/C++/Matlab, which they have used for developing ML applications. This tutorial does not require any prior knowledge in traditional software testing and quality assurance methodologies.
Key takeaways for the audience include:
We have set up a survey for the tutorial participants so that we can fine tune the contents based on the responses.
https://forms.gle/Yu5oT1ViVBPdVNVC6
This will be a half day tutorial consisting of four parts. The first part of the tutorial will cover the fundamental concepts of ML testing, followed by coverage of state of art techniques and methods in each of the sub-topics:
Duration:
Participants should bring their own laptop. We will provide a list of open source libraries to be installed for hands on exercises before the tutorial session once we finalize the contents.
This tutorial will be organized by three of us:
1.Sandya Mannarswamy, Independent NLP Research Scientist. sandyasm@gmail.com
2.Shourya Roy, Head, American Express AI Labs, shourya.roy@gmail.com
3.Saravanan Chidambaram, Independent NLP Researcher & Consultant, sarochida@gmail.com
Sandya Mannarswamy is an independent NLP researcher. She was previously a senior research scientist at Conduent Labs India in the Natural Language Processing research group. She holds a Ph.D. in computer science from Indian Institute of Science, Bangalore. Her research interests span natural language processing, machine learning and compilers. Her research career spans over 16 years, at various R&D labs, including Hewlett Packard Ltd, IBM Research etc. She has co-organized a number of workshops including workshops at International Conference on Data Management, Machine Learning Debates workshop at ICML-2018 etc. Her current research is focused on software testing and evaluation of Natural Language Processing applications. She has extensive experience in traditional software engineering, working on Research and Development of developer tools eco-system such as compiler, debugger, performance analyzer, static source code analyzer during her extensive career at Hewlett Packard. She along with Shourya, co-authored a paper at IJCAI 2018, which focused on the challenges in taking AI applications from research to real world. Her current research is focussed on rigorous evaluation of NLP applications (using NLP to evaluate NLP). She is the author of the popular CodeSport column in Open Source For You magazine. (https://opensourceforu.com/tag/codesport/).
Shourya Roy (https://www.linkedin.com/in/shouryaroy/) is Head and VP of American Express AI Labs which is spearheading innovations in the areas of machine learning, NLP and document recognition, cloud computing and AI-product management for American Express. Shourya’s research interest spans Text and Web Mining & Natural Language Processing. He holds a Ph.D. in computer science from Indian Institute of Science, Bangalore. Over the years, Shourya’ s work has led to 15 granted patents and about 70 publications in premier journals and conferences from his current and prior association with research labs of IBM and Xerox over 15 years. In recent times, Shourya co-organized a number of workshops in tier-1 conferences ICML 2018, KDD 2018, SIGMOD 2016-18, ECML 2016, ICDE 201617 and notably had co-initiated and ran the series of Noisy Text Analytics (AND) series of workshops between 2007-12. He is currently serving as the Vice Chair of the India Chapter of SIGKDD organization (IKDD).
Saravanan Chidambaram (Saro) (https://www.linkedin.com/in/saravanan-chidambaram-saro-9a87ab5/) is an independent consultant in Machine Learning and AI technologies. Previously he was head of Advanced Development Centre, Hewlett Packard Enterprise, where he led the research team exploring emerging technologies, including AI/Blockchain/ML. Over a career spanning 16 years, at various R&D labs, including Hewlett Packard Ltd, Microsoft and Oracle, he has led the development of many research and development projects in the areas of virtualization, compilers, kernel and big data, focusing on designing and deploying mission critical enterprise software. Saro is passionate about educating the emerging ML software developer community into adopting rigorous software quality assurance techniques. He is currently working on developing a test-suite for testing NLP applications.
Hosted by
{{ gettext('Login to leave a comment') }}
{{ gettext('Post a comment…') }}{{ errorMsg }}
{{ gettext('No comments posted yet') }}