I had the pleasure of presenting ‘Running ML Inference Services in Shared Hosting Environments’ at MLOps: Machine Learning in Production Bay Area Virtual Conference. This presentation was based off the 6 years of experience the Nextdoor CoreML team has productionalizing and operating 30+ real-time ML microservices.
I had the pleasure of presenting ‘Modern Agile for Machine Learning’ at Toronto Machine Learning Micro-Summit Series to 100+ industry Machine Learning practitioners. The talk was based off my personal experiences in applying Extreme Programming practices for enterprise ML projects at Dessa.
I had the pleasure of presenting the state-of-the-art NLP model, Google BERT, at the Toronto Deep Learning Series (TDLS) meetup. This video has 78000+ views as of Sep 2021, and is the top viewed lecture of TDLS (now called AISC).
In 2014, I entered the University of Toronto as an undergrad with a burning passion for physics. In 2018, I left the academic world to start a career in industry machine learning.
This is how I transitioned from academia to industry.
What is UofT like? Is it hard? Is it as depressing as people say it is? And what is POSt?
As a recent Bachelor of Science graduate, I answer these and more in this guide for new students.
At ZeroGravityLabs, Taraneh Khazaei and I co-authored a fantastic blog post that details resolutions of common Spark performance issues. It was featured on the Roaring Elephant - Bite-Sized Big Data podcast.
I had the pleasure of presenting how to set up Spark with Jupyter on AWS at Toronto Apache Spark #19.
HackOn(Data), Toronto’s very own data hackathon in the heart of downtown, is back for 2017!
At HackOn(Data) last year, I learned a lot, had lots of fun, and made industry connections that landed me and my teammate great summer internships (my blog post). This year I plan on volunteering for HackOn(Data) 2017.
I highly recommend HackOn(Data). Register at hackondata.com/2017!
Over the past school year (2016-2017), I have been participating in kaggle competitions with the University of Toronto Data Science Team (UDST).
We have participated in competitions such as the Outbrain Click Prediction, DSTL Satellite Imagery Feature Detection and Data Science Bowl 2017. I have learned a lot from my participation in UDST. In fact, it was these competitions that led me to write my spark-Jupyter-AWS guide, and the posts on multi-cpu data processing and s3 data access with boto3.
If you are a UofT student or simply a data enthusiast in the Toronto area, come check us out! We will be continuing activities in the summer of 2017.
