Sep 20 | 05 min read
Our Chief Data Officer, Rohit Agarwal, recently attended a conference “Predictive Analytics World” in Las Vegas, U.S. We are proud to say that he presented a talk at the conference and was also a part of the panel discussion. He believes that conferences in general are a good way to understand where the technology is headed, and what are some of the practical issues that people are facing while using the technology. It also helps in showcasing the impact that we are creating through technology in our organization.
The conference “Predictive Analysis World” is hosted 3 times a year and is one of the most sought-after conferences among ML/AI enthusiasts. This time around, 700+ folks attended the conference from all over the world. The conference was focused on 4 major tracks i.e. Finance, Healthcare, Deep Learning, and Business Applications.
I also got an opportunity to present at the conference about our work. My talk was titled “Pull Requests Analytics: A quantitative way for performance evaluation of Development Team”.
Below is the abstract that I submitted:
“Pull requests (PRs) have become de facto standard for code review/merge process by development teams using SCM tools like Github and Bitbucket. PR is a rich source of information about developers & reviewers. PRs can give us quite a lot of insights about the coding styles, and logical skills of the developers as every single line of code is being reviewed and bad smells are getting highlighted by the reviewer. The comments/suggestions that the reviewer gives, help in understanding the proficiency of the reviewer. We have developed a set of PR Analytics by applying Transformers-based NLP, Decision Trees & Statistical Analysis on PR data. PR Analytics can be used to perform skill assessments in order to find out the areas of improvement for the development team in a quantitative manner. PR Analytics can also help the Scrum masters & the project managers to better plan their deliverables since now they know the strengths & weaknesses of the development team and can allocate the right developers for the right type of tasks.”
I also got an opportunity to be a part of a panel discussion on the topic “Will DL take over ML entirely or will it always remain overkill for some projects”.
The first experience of being a part of any panel discussion was simply amazing. In total, there were 3-panel members and we had to present our thoughts on the topic followed by a 30 mins Q&A from the audience.
Some of the points that I discussed as a panel member:
One Don’t:If the customer is just starting up, showing them DL and complex algos will scare them away. Start with simple ML algos that bring out some improvements in the existing ways and then once the confidence is built, start giving them complex models and so on.
One Do: Use DL when you are dealing with a lot of different data sets that you have no idea what is good and what is bad. Basically, when you don’t have any expertise and still want to come up with a reasonable output.
Overall, the experience as a panel member was really great and I hope to be a part of such panel discussions in the future as well.
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